About impact.com

impact.com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products—Performance (affiliate), Creator (influencer), and Advocate (customer referral)—unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands, including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics, rely on impact.com to power more than 225,000 partnerships that deliver measurable business results.

About the Role

We're seeking a Senior Data Scientist specializing in Fraud and Risk to join our Cape Town Data Science team. In this role, you'll be at the forefront of protecting our affiliate marketing ecosystem by researching, developing, and deploying ML models that detect and prevent fraud across attribution, lead quality, and partner compliance. You'll work on high-impact problems spanning traditional fraud patterns and emerging threats—from attribution manipulation to browser extension abuse—while building production systems that scale. This is an opportunity to combine rigorous analytical work with tangible business impact in a fast-moving, adversarial domain.

Core Responsibilities

Research & model development

• Conduct R&D on fraud detection and risk monitoring across the digital advertising ecosystem, including attribution fraud, lead fraud, click injection, browser extension abuse (e.g., Honey-style coupon hijacking), brand safety violations, and creator authenticity verification.
• Design, prototype, and validate ML models and rule-based systems for fraud detection, partner risk scoring, compliance monitoring, and trust & safety workflows.
• Research and apply graph-based fraud detection techniques (community detection, link analysis, behavioral clustering) and explore graph database applications for modeling relationships between users, devices, transactions, and partners to uncover coordinated fraud rings and suspicious network patterns.
• Stay ahead of emerging fraud patterns through continuous learning—monitoring industry trends, reviewing academic literature, exploring data for novel anomalies, and collaborating closely with Product, Compliance, and Trust & Safety teams.

Production deployment & iteration

• Deploy Fraud and Risk ML models to production; own the end-to-end delivery from ETL, feature engineering, model training, deployment, to monitoring.
• Iterate on live models by adding new features, improving performance (precision/recall/F1), and reducing false positives.
• Partner with MLOps and Engineering to ensure models are robust, scalable, and production-ready (testing, alerts, drift monitoring, retraining pipelines).

Data analytics & insights

• Perform deep-dive analyses on fraud trends, partner behavior, and risk patterns to inform model strategy and business decisions.
• Translate analytical findings into actionable recommendations for Product, Marketing, and Finance stakeholders.
• Build dashboards and reports to communicate model performance, fraud impact, and risk metrics to leadership.

Cross-functional collaboration

• Work closely with Product, Engineering, Compliance, and Finance to scope requirements, prioritize work, and align on success metrics.
• Communicate technical work clearly to non-technical audiences; present findings and tradeoffs in planning forums and reviews.
• Contribute to a culture of experimentation, documentation, and knowledge sharing within the Data Science team.

Requirements

Essential

• Experience: 5+ years in data science, ML, or advanced analytics, with at least 2+ years focused on fraud detection, risk modeling, or anomaly detection in production environments.
• Fraud & risk domain expertise: Demonstrated experience building and deploying fraud or risk models (classification, anomaly detection, time-series analysis, graph-based methods).
• Technical skills:
• Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, or similar).
• Experience with feature engineering, model evaluation (ROC/AUC, precision-recall, cost-sensitive learning), and handling imbalanced datasets.
• Familiarity with production ML workflows (versioning, monitoring, A/B testing, model retraining).
• Analytical rigor: Strong foundation in statistics and ML; ability to design experiments, validate models, and interpret results with business context.
• Communication: Ability to translate complex technical work into clear insights for stakeholders; experience presenting to cross-functional teams.
• Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, or similar); Master's/PhD preferred.

Preferred / Nice to have

• Experience in affiliate marketing, ad tech, or e-commerce fraud (attribution fraud, click fraud, lead validation, coupon abuse).
• Familiarity with browser extension detection, fingerprinting, or device/user identity resolution.
• Experience with graph analytics or network-based fraud detection (community detection, link analysis, behavioral clustering).
• Knowledge of privacy-preserving ML techniques or working with privacy-constrained data.
• Experience with real-time or near-real-time scoring and low-latency deployment (e.g., REST APIs, streaming pipelines).
• Familiarity with GCP tools (BigQuery, Vertex AI, Cloud Run) and/or Databricks/Spark for large-scale data processing.
• Exposure to rule engines, decision trees, or hybrid rule-ML systems for compliance and risk workflows.

What sets you apart

• Adversarial thinking: You understand how fraudsters operate and can anticipate evasion tactics and evolving attack vectors.
• Pragmatic delivery: You balance rigor with speed, prioritizing MVPs and iterative improvement over perfection.
• Business impact orientation: You focus on measurable outcomes (fraud loss reduction, false positive rates, operational efficiency) and communicate ROI clearly.
• Comfort with ambiguity: You thrive in evolving problem spaces, defining your own roadmap when fraud patterns shift or new threats emerge.
• Collaboration and influence: You build trust across teams and can drive adoption of your models through enablement, documentation, and clear storytelling.

Benefits and Perks: 

At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a benefits package that supports your well-being, growth, and work-life balance.

• Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life.
• Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health.
• A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.
• Investing in Your Growth: We’re committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses.
• Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.
• Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses

impact.com is proud to be an equal opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.

About us:

Luno is the crypto investment app you can rely on, enabling you to buy, store and explore crypto securely. We’re committed to putting the power of cryptocurrency in everyone's hands sensibly and responsibly.

Since 2013, we’ve helped millions of people around the world invest safely in crypto. We do this by cutting through the hype and supporting customers at every step of the way. All products and cryptocurrencies on our platform must first meet stringent legal, risk and technical security standards. 

Transparency is key for us. Luno stores all crypto on a 1:1 basis and we have rigorous processes in place so you can be confident your investment is secure. We’re available in more than 40 countries around the world and we work closely with respective regulators in all of these markets to be fully compliant, as we believe this is the best way to help everyone, everywhere, invest safely.

The role in a nutshell:

You will form part of the Onboarding Operations team based in Cape Town and Kuala Lumpur, reporting to the Head of Onboarding Strategy and Customer Due Diligence Operations. Your role as a Business Optimisation Analyst (Data) will be to use your knowledge of data analysis and data processing to inform key business decisions in the Onboarding Operations team and other business areas. You will work in a fast paced environment, collaborating with other business units, including Engineering, Product, Compliance and Data teams, servicing the business on an international level together with the teams from South East Asia, Africa and Europe. You will be responsible for creating dashboards, extracting data, writing reports, and monitoring key performance indicators (KPIs) to determine business initiatives’ success.

Your mission will be:

• Interpret trends and patterns in data and present findings and actionable insights to senior leaders of the business
• Collaborate with data engineers to design and build any pipelines/data models needed to support analytical and reporting needs
• Structure large data sets to find usable information
• Design metrics to help assess how the onboarding teams and broader business are performing on a day-to-day basis
• Report regularly on business and team performance
• Work closely with the relevant business stakeholders to understand their data needs
• Collaborate with teams to determine, collect and analyse required data to address business needs while ensuring it is of the highest quality, reliable and trusted
• Build visually compelling dashboards that communicate insights to stakeholders (often non-technical) to allow data-informed decision making
• Use data-driven analysis to identify opportunities for improvement and automation in onboarding operations processes
• Review delivery of providers against Service Level Agreements and support negotiations from a data perspective

The ideal candidate will have:

• A qualification in Data Analysis or similar quantitative fields.
• A minimum of 1 year of experience in the data or business optimisation space
• Intermediate skills in SQL, Python and experience working with data pipelines.
• Experience working with business intelligence platforms such as Looker, Tableau, DataBricks, PowerBI or similar
• Familiar with data governance principles and best practices
• Passionate about data analytics and related disciplines
• Friendly, transparent, articulate and driven to succeed.
• Experience in a customer service / customer onboarding / KYC environment is advantageous
• Ability to lead change through entrepreneurial thinking, a strong sense of responsibility and a very high level of integrity
• Adaptable, capable and intellectually curious and have the persistence to find answers to difficult questions in fast-paced and environments
• Ability to formulate business cases, document investigation work, and contribute to technical design documents
• Ability to take ownership of own work and successfully deliver high quality projects with obscure and incomplete requirements
• Strong interpersonal and communication skills and ability to explain data concepts to non-experts in a clear, and structured manner
• Experience working with cross-functional teams using data to influence strategy, roadmaps and drive data-informed decisions
• Internet access at home and the ability to work remotely (Luno will provide successful candidates with suitable equipment to set them up for success)

Life at Luno:

• Remote but reachable work policy gives you the freedom to choose between working from home or the office.
• Plus the option to buy and sell up to 5 days leave
• Improve body and mind, with excellent private medical insurance
• Access to Learnably and our additional learning platforms for your personal and professional development
• 6 months primary care-giver leave
• Paw-ternity leave for your furry friend
• Annual Inspiration Day in addition to your annual leave which increases based on your length of service! 
• A collaborative, friendly work community, with regular social events and virtual cooking, dancing, drawing and house planting classes hosted by our Lunauts
• Free lunch and snacks
• 0 fees up to a certain amount with Luno from the day you start.*

*Offer only available on Luno Instant Buy, not Luno Exchange.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

About us:

Luno is the crypto investment app you can rely on, enabling you to buy, store and explore crypto securely. We’re committed to putting the power of cryptocurrency in everyone's hands sensibly and responsibly.

Since 2013, we’ve helped millions of people around the world invest safely in crypto. We do this by cutting through the hype and supporting customers at every step of the way. All products and cryptocurrencies on our platform must first meet stringent legal, risk and technical security standards. 

Transparency is key for us. Luno stores all crypto on a 1:1 basis and we have rigorous processes in place so you can be confident your investment is secure. We’re available in more than 40 countries around the world and we work closely with respective regulators in all of these markets to be fully compliant, as we believe this is the best way to help everyone, everywhere, invest safely.

The role in a nutshell:

You will form part of the Onboarding Operations team based in Cape Town and Kuala Lumpur, reporting to the Head of Onboarding Strategy and Customer Due Diligence Operations. Your role as a Business Optimisation Analyst (Data) will be to use your knowledge of data analysis and data processing to inform key business decisions in the Onboarding Operations team and other business areas. You will work in a fast paced environment, collaborating with other business units, including Engineering, Product, Compliance and Data teams, servicing the business on an international level together with the teams from South East Asia, Africa and Europe. You will be responsible for creating dashboards, extracting data, writing reports, and monitoring key performance indicators (KPIs) to determine business initiatives’ success.

Your mission will be:

• Interpret trends and patterns in data and present findings and actionable insights to senior leaders of the business
• Collaborate with data engineers to design and build any pipelines/data models needed to support analytical and reporting needs
• Structure large data sets to find usable information
• Design metrics to help assess how the onboarding teams and broader business are performing on a day-to-day basis
• Report regularly on business and team performance
• Work closely with the relevant business stakeholders to understand their data needs
• Collaborate with teams to determine, collect and analyse required data to address business needs while ensuring it is of the highest quality, reliable and trusted
• Build visually compelling dashboards that communicate insights to stakeholders (often non-technical) to allow data-informed decision making
• Use data-driven analysis to identify opportunities for improvement and automation in onboarding operations processes
• Review delivery of providers against Service Level Agreements and support negotiations from a data perspective

The ideal candidate will have:

• A qualification in Data Analysis or similar quantitative fields.
• A minimum of 1 year of experience in the data or business optimisation space
• Intermediate skills in SQL, Python and experience working with data pipelines.
• Experience working with business intelligence platforms such as Looker, Tableau, DataBricks, PowerBI or similar
• Familiar with data governance principles and best practices
• Passionate about data analytics and related disciplines
• Friendly, transparent, articulate and driven to succeed.
• Experience in a customer service / customer onboarding / KYC environment is advantageous
• Ability to lead change through entrepreneurial thinking, a strong sense of responsibility and a very high level of integrity
• Adaptable, capable and intellectually curious and have the persistence to find answers to difficult questions in fast-paced and environments
• Ability to formulate business cases, document investigation work, and contribute to technical design documents
• Ability to take ownership of own work and successfully deliver high quality projects with obscure and incomplete requirements
• Strong interpersonal and communication skills and ability to explain data concepts to non-experts in a clear, and structured manner
• Experience working with cross-functional teams using data to influence strategy, roadmaps and drive data-informed decisions
• Internet access at home and the ability to work remotely (Luno will provide successful candidates with suitable equipment to set them up for success)

Life at Luno:

• Remote but reachable work policy gives you the freedom to choose between working from home or the office.
• Plus the option to buy and sell up to 5 days leave
• Improve body and mind, with excellent private medical insurance
• Access to Learnably and our additional learning platforms for your personal and professional development
• 6 months primary care-giver leave
• Paw-ternity leave for your furry friend
• Annual Inspiration Day in addition to your annual leave which increases based on your length of service! 
• A collaborative, friendly work community, with regular social events and virtual cooking, dancing, drawing and house planting classes hosted by our Lunauts
• Free lunch and snacks
• 0 fees up to a certain amount with Luno from the day you start.*

*Offer only available on Luno Instant Buy, not Luno Exchange.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Our Company:

At impact.com we are passionate about our people, our technology, and are obsessed with customer success. Working together enables us to grow rapidly, win, and serve the largest brands in the world. We use cutting edge technology to solve real-world problems for our clients and continue to pull ahead of the pack as the leading SaaS platform for businesses to automate their partnerships and grow their revenue like never before. We have an entrepreneurial spirit and a culture where ambition and curiosity is rewarded. If you are looking to join a team where your opinion is valued, your contributions are noticed, and enjoy working with fun and talented people from all over the world, then this is the place for you!

impact.com, the world’s leading partnership management platform, is transforming the way businesses manage and optimize all types of partnerships—including traditional rewards affiliates, influencers, commerce content publishers, B2B, and more. The company’s powerful, purpose-built platform makes it easy for businesses to create, manage, and scale an ecosystem of partnerships with the brands and communities that customers trust to make purchases, get information, and entertain themselves at home, at work, or on the go. To learn more about how impact.com’s technology platform and partnerships marketplace is driving revenue growth for global enterprise brands such as Walmart, Uber, Shopify, Lenovo, L’Oreal, Fanatics and Levi’s, visit www.impact.com.

Your Role at impact.com:

impact.com's Analytics Data Platform processes terabyte-scale data for one of the world's leading partnership SaaS platforms, covering the full lifecycle from ingestion to governed access.

We're looking for a technical people leader to own and grow the Data Platform Engineering team, while driving the architecture, reliability, and evolution of impact.com's Analytics Data Platform. This is a senior player-coach role: you'll combine hands-on technical leadership with direct people management, building a high-performing team, owning delivery, and shaping platform strategy. Expect to spend roughly 40% hands-on and 60% on leadership, people, and delivery, shifting as the team matures. You'll be accountable for both the team's output and its people, ensuring engineers are supported, challenged, and growing.

As Team Lead for Data Platform Engineering, you'll work across the full data ecosystem (ingestion, processing, storage, orchestration, access, and governance) using technologies like Scala, Python, Google Cloud Dataproc, BigQuery, and Airflow. You'll shape the operating model for the team, establish engineering standards, and drive platform strategy. This role is levelled at Senior/Staff Engineer on the engineering management track and reports to the Director of Advanced Analytics. The position is based in Cape Town, hybrid, with one day per week in office.

Data Platform Engineering is one of four sub-teams within the Data Analytics Group (DAG). DPE owns ingestion, infrastructure, orchestration, and platform services. Analytics Engineering owns the dbt transformation layer and semantic layer. Data Analytics owns reporting and insights. Data Product Management owns product roadmaps and governance processes. You'll partner closely with all three, but won't own dbt models, reporting, or product prioritisation directly.

Our ideal candidate combines distributed systems expertise with proven people leadership. Someone who can make architecture decisions, run a team, and translate platform trade-offs into business language. This role is for someone who wants to build both a platform and a team, not just maintain what exists.

What you'll inherit:

The platform has strong foundations: Scala/Spark pipelines processing TB-scale data, 37+ Fivetran connectors, Airflow on Astronomer for orchestration, a mature dbt-based transformation layer, and BigQuery as the core analytical warehouse. Databricks is part of the current stack but is being transitioned off in favour of Dataproc and BigQuery-native patterns. What's greenfield: governance frameworks, data contracts, SLOs/SLAs, observability, cost management, and data quality monitoring. You'll build the team that delivers on these opportunities.

What you'll do:

• Build and run a team of data platform engineers: set direction, remove blockers, give honest feedback, and make sure people are growing in their careers.
• Create an environment where engineers do their best work: high trust, high standards, low friction.
• Turn platform strategy into a delivery plan the team can actually execute: sequence the work, balance new builds against keeping the lights on, and make the trade-offs visible.
• Own capacity and priorities across the team. Keep cross-team dependencies from becoming bottlenecks.
• Lead platform architecture decisions, design reviews, and cross-org technical coordination, defining and promoting architectural patterns, standards, and guidelines.
• Design and implement scalable pipelines using Scala, Python, and distributed frameworks (Spark on Dataproc) processing TB-scale data. Lead the migration off Databricks onto Dataproc and BigQuery-native patterns.
• Establish platform reliability practices: define SLOs/SLAs, build incident management and response processes, and drive systemic improvements across pipeline and infrastructure operations.
• Design and maintain core platform services (Dataproc clusters, BigQuery datasets, Cloud Storage) and optimise platform performance and cost at scale.
• Build and operationalise data governance, security, and compliance capabilities, including data contracts, access controls, and audit logging. Many of these practices are greenfield; you will be building them from scratch.
• Define and evolve team rituals, operating cadences, and engineering standards: code review norms, documentation practices, on-call fairness, and knowledge sharing.
• Represent the team to senior leadership and act as the single point of accountability for the team's commitments to cross-functional partners (analytics engineering, data analytics, product, finance).
• Own data source integrations and data quality across the platform, including third-party connectors (e.g. Fivetran), custom Scala/Spark ingestion pipelines, dbt orchestration via Airflow and Astronomer Cosmos, and quality monitoring and remediation practices.

Required Skills & Experience:

• 7+ years of experience in data platform engineering, data engineering, or distributed systems, including 2+ years leading engineering teams
• Strong programming skills in Scala and Python, including functional Scala patterns (cats-effect / Typelevel ecosystem), testing, code review, and production-grade engineering practices
• Deep hands-on experience with distributed computing frameworks: Apache Spark (Dataproc) and large-scale data processing. Databricks experience is useful for leading the migration off it
• Expert-level knowledge of cloud-native data platforms and services. Our stack runs on Google Cloud Platform (BigQuery, Dataproc, Cloud Storage), but equivalent depth on AWS or Azure is equally valued
• Advanced SQL skills including complex query optimisation, partitioning strategies, and performance tuning
• Experience building and scaling engineering teams, not just managing established ones: hiring, running interview loops, onboarding, and growing engineers from junior to senior
• Experience leading cross-team architecture initiatives and establishing platform-wide standards
• Experience with orchestration tools (Airflow, Astronomer, and Astronomer Cosmos for dbt orchestration) and CI/CD pipeline design
• Familiarity with legacy CI/CD tooling (e.g. Jenkins), particularly in the context of pipeline migration and modernisation
• Strong understanding of data governance, security best practices, and compliance requirements
• Experience with infrastructure as code and automated deployment practices
• Excellent communication skills, with the ability to translate technical trade-offs for non-technical stakeholders and represent the team to senior leadership
• Strong people management skills: coaching, mentorship, performance management, and career development
• Experience with monitoring, alerting, observability, and on-call operations for production data systems

Nice to Have:

• Experience in the digital marketing technology or partnership/affiliate industry
• General experience with Google Cloud Platform services beyond the core required stack (e.g. Pub/Sub, BigTable, Cloud Functions); familiarity with Dataflow is a plus but not a current platform requirement (it is under future evaluation)
• Familiarity with streaming technologies such as Apache Kafka and stream processing frameworks
• Experience standing up data quality frameworks and monitoring practices from scratch
• Experience with FinOps or cloud cost management at scale
• Experience migrating off legacy platforms (e.g. Databricks, on-prem Hadoop) to cloud-native alternatives
• Experience with organisational design, team scaling, or building engineering teams from scratch
• Awareness of AI agent architectures and how they connect to data platforms (e.g. MCP, tool-use patterns)
• Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)

Benefits:

• Hybrid, Casual work environment
• Responsible PTO policy
• Take the time off that you need. We are truly committed to a positive work-life balance, recognising that it is important to be happy and fulfilled in both

• Training & Development
• Learning the advanced partnership automation products
• Medical Aid and Provident Fund 
• Group schemes with Discovery & Bonitas for medical aid
• Group scheme for provident fund
• Restricted Stock Units
• 3-year vesting schedule pending Board approval
• Internet Allowance
• Fitness club fee reimbursementsTechnology stipend 
• Primary Caregiver Leave
• Mental  Health and Wellness Benefit - Including 12 Therapy/Coaching sessions + Dependent coverage 

impact.com is proud to be an equal opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.

About impact.com

impact.com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products—Performance (affiliate), Creator (influencer), and Advocate (customer referral)—unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands, including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics, rely on impact.com to power more than 225,000 partnerships that deliver measurable business results.

About the Role

We're seeking a Senior Data Scientist specializing in Fraud and Risk to join our Cape Town Data Science team. In this role, you'll be at the forefront of protecting our affiliate marketing ecosystem by researching, developing, and deploying ML models that detect and prevent fraud across attribution, lead quality, and partner compliance. You'll work on high-impact problems spanning traditional fraud patterns and emerging threats—from attribution manipulation to browser extension abuse—while building production systems that scale. This is an opportunity to combine rigorous analytical work with tangible business impact in a fast-moving, adversarial domain.

Core Responsibilities

Research & model development

• Conduct R&D on fraud detection and risk monitoring across the digital advertising ecosystem, including attribution fraud, lead fraud, click injection, browser extension abuse (e.g., Honey-style coupon hijacking), brand safety violations, and creator authenticity verification.
• Design, prototype, and validate ML models and rule-based systems for fraud detection, partner risk scoring, compliance monitoring, and trust & safety workflows.
• Research and apply graph-based fraud detection techniques (community detection, link analysis, behavioral clustering) and explore graph database applications for modeling relationships between users, devices, transactions, and partners to uncover coordinated fraud rings and suspicious network patterns.
• Stay ahead of emerging fraud patterns through continuous learning—monitoring industry trends, reviewing academic literature, exploring data for novel anomalies, and collaborating closely with Product, Compliance, and Trust & Safety teams.

Production deployment & iteration

• Deploy Fraud and Risk ML models to production; own the end-to-end delivery from ETL, feature engineering, model training, deployment, to monitoring.
• Iterate on live models by adding new features, improving performance (precision/recall/F1), and reducing false positives.
• Partner with MLOps and Engineering to ensure models are robust, scalable, and production-ready (testing, alerts, drift monitoring, retraining pipelines).

Data analytics & insights

• Perform deep-dive analyses on fraud trends, partner behavior, and risk patterns to inform model strategy and business decisions.
• Translate analytical findings into actionable recommendations for Product, Marketing, and Finance stakeholders.
• Build dashboards and reports to communicate model performance, fraud impact, and risk metrics to leadership.

Cross-functional collaboration

• Work closely with Product, Engineering, Compliance, and Finance to scope requirements, prioritize work, and align on success metrics.
• Communicate technical work clearly to non-technical audiences; present findings and tradeoffs in planning forums and reviews.
• Contribute to a culture of experimentation, documentation, and knowledge sharing within the Data Science team.

Qualifications

Required

• Experience: 5+ years in data science, ML, or advanced analytics, with at least 2+ years focused on fraud detection, risk modeling, or anomaly detection in production environments.
• Fraud & risk domain expertise: Demonstrated experience building and deploying fraud or risk models (classification, anomaly detection, time-series analysis, graph-based methods).
• Technical skills:
• Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, or similar).
• Experience with feature engineering, model evaluation (ROC/AUC, precision-recall, cost-sensitive learning), and handling imbalanced datasets.
• Familiarity with production ML workflows (versioning, monitoring, A/B testing, model retraining).
• Analytical rigor: Strong foundation in statistics and ML; ability to design experiments, validate models, and interpret results with business context.
• Communication: Ability to translate complex technical work into clear insights for stakeholders; experience presenting to cross-functional teams.
• Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, or similar); Master's/PhD preferred.

Preferred / Nice to have

• Experience in affiliate marketing, ad tech, or e-commerce fraud (attribution fraud, click fraud, lead validation, coupon abuse).
• Familiarity with browser extension detection, fingerprinting, or device/user identity resolution.
• Experience with graph analytics or network-based fraud detection (community detection, link analysis, behavioral clustering).
• Knowledge of privacy-preserving ML techniques or working with privacy-constrained data.
• Experience with real-time or near-real-time scoring and low-latency deployment (e.g., REST APIs, streaming pipelines).
• Familiarity with GCP tools (BigQuery, Vertex AI, Cloud Run) and/or Databricks/Spark for large-scale data processing.
• Exposure to rule engines, decision trees, or hybrid rule-ML systems for compliance and risk workflows.

What sets you apart

• Adversarial thinking: You understand how fraudsters operate and can anticipate evasion tactics and evolving attack vectors.
• Pragmatic delivery: You balance rigor with speed, prioritizing MVPs and iterative improvement over perfection.
• Business impact orientation: You focus on measurable outcomes (fraud loss reduction, false positive rates, operational efficiency) and communicate ROI clearly.
• Comfort with ambiguity: You thrive in evolving problem spaces, defining your own roadmap when fraud patterns shift or new threats emerge.
• Collaboration and influence: You build trust across teams and can drive adoption of your models through enablement, documentation, and clear storytelling.

Benefits and Perks:

At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a benefits package that supports your well-being, growth, and work-life balance.

• Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life.
• Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health.
• A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.
• Investing in Your Growth: We’re committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses.
• Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.
• Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses

impact.com is proud to be an equal opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.

About impact.com

impact.com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products—Performance (affiliate), Creator (influencer), and Advocate (customer referral)—unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands, including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics, rely on impact.com to power more than 225,000 partnerships that deliver measurable business results.

About the Role

We're seeking a Senior Data Scientist specializing in Product Data Quality to join our Cape Town Data Science team. In this role, you'll own the analytical and technical foundation of product data quality across our ecosystem—spanning catalog hygiene, transaction matching, classification modeling, deduplication, and global product identity. You'll work across both the structured catalog universe and the messier, larger-scale sales transaction universe, building models and infrastructure that power search, recommendations, and business intelligence. This is a high-impact role that demands both analytical depth and strong engineering capabilities: you'll take models from research to production, build scalable data pipelines, and create the monitoring infrastructure that makes our product data foundation trustworthy and continuously improving. Your work will directly influence search relevance, recommendation quality, match rates, and reporting accuracy across the business.

Core Responsibilities

Product classification & taxonomy modeling

• Develop, deploy, and maintain ML models for automated product categorization and taxonomy assignment across hierarchical category structures.
• Improve classification accuracy through feature engineering (text, attributes, embeddings), model iteration, and robust evaluation on both catalog and sales transaction data.
• Monitor production model performance; identify and remediate misclassification patterns that impact search, recommendations, and reporting.
• Collaborate with category experts and Product teams to refine taxonomy definitions, handle edge cases, and adapt to new product types.

Catalog & sales universe data quality

• Conduct deep-dive analyses into catalog completeness, consistency, and correctness across retailers, categories, and product attributes.
• Own data quality analytics for the sales transaction universe—a larger, messier dataset than catalog—measuring match rates, diagnosing gaps (unmatched transactions, misattributed products), and identifying systematic failures.
• Define and track catalog and transaction health KPIs (attribute coverage, schema compliance, match rates, GPID coverage, freshness); identify root causes and drive remediation.
• Build monitoring systems and dashboards to track data quality trends across retailers, categories, and time periods.

Global Product ID (GPID) coverage & matching

• Assess GPID (GTIN/UPC/EAN) coverage and accuracy across both catalog and sales transaction data; identify gaps by category, retailer, and brand.
• Build and improve matching algorithms to link sales transactions to catalog products, handling missing GPIDs, naming inconsistencies, and category misclassification.
• Quantify the impact of GPID enrichment and matching improvements on search, deduplication, and reporting accuracy.
• Partner with external data providers and brands to improve GPID coverage and resolve identifier conflicts.

Deduplication & entity resolution

• Identify product variants (size, color, packaging) and duplicates within and across retailer catalogs using clustering, entity resolution, embeddings, and similarity-based techniques.
• Build scalable deduplication pipelines that handle catalog and transaction data at scale; define patterns, heuristics, and ML-based approaches for variant grouping.
• Measure the impact of deduplication on search quality, recommendation accuracy, and reporting; iterate on models to reduce false positives and improve precision.
• Support Data Engineering and Platform teams in productionizing deduplication and entity linking infrastructure.

Manufacturer data quality & brand engagement

• Evaluate the consistency and accuracy of manufacturer-level attributes (brand name, MPN, manufacturer identifiers) across catalogs and transactions.
• Detect systemic issues at the brand and retailer level; build scorecards and engage brands (via the Tiger Team) to drive data quality improvements.
• Create feedback loops to measure manufacturer data quality and track progress on remediation initiatives.

Product search & retrieval infrastructure

• Research and prototype improvements to product search and retrieval pipelines, including vector search, semantic similarity, and embedding-based matching.
• Explore and implement vector database infrastructure (e.g., FAISS, Pinecone, Weaviate) to support fast, scalable product retrieval and similarity search.
• Contribute to the design and optimization of retrieval pipelines that combine text, attributes, and embeddings for search and recommendations.
• Evaluate search relevance and ranking quality; iterate on indexing strategies, query preprocessing, and re-ranking models.

Product graph & relational modeling

• Build and maintain product graph infrastructure that captures relationships between products, variants, brands, categories, retailers, and transactions.
• Use graph-based techniques (community detection, link analysis, centrality) to identify product families, detect duplicates, and surface insights on product hierarchies.
• Partner with Data Platform teams to design scalable graph storage and query patterns (e.g., Neo4j, graph extensions in BigQuery).

Insights, monitoring & reporting

• Systematically identify, classify, and prioritize product data quality issues; create clear summaries, visualizations, and actionable recommendations for stakeholders.
• Build and maintain dashboards and recurring reports for key product data KPIs (match rates, GPID coverage, duplicate rates, classification accuracy, attribute completeness).
• Establish alerting and anomaly detection systems to proactively surface data quality degradation and model performance issues.

Engineering & production deployment

• Take models and analytics prototypes from POC to production, with or without engineering partnership—owning deployment, testing, monitoring, and iteration.
• Build robust, scalable data pipelines and ML workflows using production-grade tools and best practices (versioning, CI/CD, testing, observability).
• Collaborate with MLOps and Data Engineering teams to ensure production readiness: reliability, latency, drift monitoring, and SLOs.

Qualifications

Required

• Experience: 5+ years in data science, ML engineering, or analytics engineering, with at least 2+ years focused on product data, catalog quality, entity resolution, search/retrieval, or e-commerce/marketplace analytics.
• Engineering strength: Proven ability to build production-grade data pipelines and deploy ML models independently; strong software engineering fundamentals (code quality, testing, version control, CI/CD).
• Data quality expertise: Demonstrated experience analyzing and improving large-scale structured data quality (completeness, consistency, accuracy, deduplication, entity resolution).
• ML & classification experience: Track record building and deploying classification models, ranking systems, or search/retrieval pipelines in production.
• Technical skills:
• Strong Python and SQL; proficiency with ML libraries (scikit-learn, XGBoost, LightGBM, PyTorch/TensorFlow) and data manipulation tools (pandas, PySpark).
• Experience with entity resolution, fuzzy matching, clustering, embeddings, and similarity-based techniques (Levenshtein distance, cosine similarity, nearest-neighbor search).
• Familiarity with production ML workflows (model versioning, monitoring, evaluation, retraining, A/B testing).
• Experience with data profiling, anomaly detection, and exploratory analysis at scale.
• Analytical rigor: Strong foundation in statistics and ML; ability to design experiments, validate models, interpret results, and communicate insights with business context.
• Stakeholder collaboration: Experience working cross-functionally with Product, Engineering, and business teams; ability to translate technical work into actionable recommendations.
• Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, or similar); Master's/PhD preferred.

Preferred / Nice to have

• Experience with vector search and embeddings (sentence transformers, OpenAI embeddings, BERT-based models) and vector databases (FAISS, Pinecone, Weaviate, Milvus, pgvector).
• Familiarity with search and retrieval systems (Elasticsearch, Solr, semantic search, BM25, hybrid ranking) and understanding how data quality impacts relevance.
• Experience with graph databases and graph analytics (Neo4j, NetworkX, graph algorithms for clustering and link prediction).
• Knowledge of NLP techniques for product data (text classification, named entity recognition, attribute extraction, title/description parsing, semantic similarity).
• Experience with multimodal modeling (combining text, images, and structured attributes for classification or retrieval).
• Familiarity with global product identifiers (GTIN/UPC/EAN, MPN, SKU hierarchies) and standards organizations (GS1, GDSN).
• Experience with deduplication and record linkage at scale (blocking strategies, probabilistic matching, hierarchical clustering).
• Familiarity with GCP tools (BigQuery, Vertex AI, Dataflow, Cloud Run, Looker) and/or Databricks/Spark for large-scale processing and deployment.
• Exposure to master data management (MDM) or data governance practices in product or catalog contexts.
• Experience with recommendation systems or understanding how product data quality impacts personalization and ranking.

What sets you apart

• Product data obsession: You care deeply about data quality and understand how poor catalog hygiene cascades into user experience, business reporting, and operational inefficiencies.
• Engineering mindset: You don't just build prototypes—you ship them. You write clean, tested, production-ready code and can own the full lifecycle from research to deployment.
• Detective instincts: You love digging into messy data, finding patterns, and uncovering root causes—whether it's a systematic retailer issue, a subtle duplicate cluster, or a classification edge case.
• Pragmatic prioritization: You balance comprehensiveness with impact, focusing on the 20% of issues that drive 80% of quality problems and business value.
• Search & retrieval intuition: You understand how product data powers search and recommendations, and you know how to build infrastructure (embeddings, vector DBs, graphs) that makes these systems work at scale.
• Stakeholder fluency: You translate messy data findings into clear, actionable recommendations and build trust with brands, retailers, Product, and Engineering teams.
• Comfort with ambiguity: You thrive in evolving data ecosystems, defining your own quality metrics and technical roadmaps when the problem space is still being shaped.

Benefits and Perks: 

At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a benefits package that supports your well-being, growth, and work-life balance.

• Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life.
• Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health.
• A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.
• Investing in Your Growth: We’re committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses.
• Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.
• Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses

impact.com is proud to be an equal opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.

About us: 

Luno is the crypto investment app you can rely on, enabling you to buy, store and explore crypto securely. We’re committed to putting the power of cryptocurrency in everyone's hands sensibly and responsibly.

Since 2013, we’ve helped millions of people around the world invest safely in crypto. We do this by cutting through the hype and supporting customers at every step of the way. All products and cryptocurrencies on our platform must first meet stringent legal, risk and technical security standards.

Transparency is key for us. Luno stores all crypto on a 1:1 basis and we have rigorous processes in place so you can be confident your investment is secure. We’re available in more than 40 countries around the world and we work closely with respective regulators in all of these markets to be fully compliant, as we believe this is the best way to help everyone, everywhere, invest safely.

About the team: 

The Data community at Luno, comprising Data Engineers, Scientists and Analysts, uncovers valuable insights from the vast amounts of data we gather from our customers and platform. These insights are crucial for guiding our product development and business strategy.

As a Product Data Scientist joining Luno, you would be part of the Product Analytics team providing high quality insights into our product through experimentation and advanced analytics.

Our current technology stack includes Databricks, Thoughtspot, Amplitude and Segment.

The role in a nutshell:

As we accelerate on our mission to put crypto in everyone’s hands, your role is to use data and insights to drive better product decisions and influence the Luno product strategy. This will involve collaborating with cross-functional teams to find, size, prioritise, evaluate and iterate on problems and opportunities to enhance our product. 

This is a hands-on product analytics role focused on experimentation, customer behaviour and data-informed decision-making. While there may be opportunities to apply modelling or machine learning techniques for specific use cases, this is not a production ML-focused role.

Your mission will be:

• Partner with cross-functional product teams including Product Managers, Designers and Engineers to provide product analytics for data-informed decisions.
• Partner with product and marketing teams to evaluate and optimise growth initiatives across acquisition, activation, retention and re-engagement.
• Empower teams with the data, tools and skills required to prioritise initiatives and shape the product roadmap.
• Design and implement event tracking data to unlock actionable insights.
• Define, understand, and test opportunities and levers to improve the product through insights and recommendations.
• Partner with product and marketing teams to design high-quality experiments, interpret results and ensure learnings translate into action.
• Apply analytical techniques such as segmentation, funnel and cohort analysis to deeply understand customer behaviours and journeys.
• Use data and analytics to inform, influence and support execution of the product strategy.

A little about you:

• You enjoy using data to understand complex customer behaviour and turn ambiguity into clear, actionable insights.
• You approach problems in a structured way, forming hypotheses, testing assumptions and iterating based on evidence.
• You’re comfortable influencing product and growth decisions without direct ownership, using data, clear reasoning and storytelling.
• You can balance speed and analytical rigour, knowing when fast, directional insight is more valuable than perfectly polished analysis.
• You communicate confidently with both technical and non-technical stakeholders, adapting your message to your audience.
• You enjoy collaborating with cross-functional partners and value collective outcomes over individual credit.
• You’re curious by nature and motivated to keep learning as tools, techniques and the data landscape evolve.

The ideal Lunaut will have:

• Typically 3+ years of experience in a data, analytics or product analytics role.
• A strong foundation in quantitative disciplines such as Statistics, Mathematics, Computer Science, Engineering or Economics (degree or equivalent practical experience).
• Experience partnering with cross-functional product teams to influence product direction, prioritisation and decision-making using data.
• Demonstrable experience designing, analysing and interpreting experiments (e.g. A/B tests) in a product environment.
• Hands-on experience working with event-level product data to understand customer behaviour and journeys.
• Strong SQL skills and experience working with relational databases.
• Experience using data visualisation and BI tools (e.g. Thoughtspot or similar) to communicate insights.
• Working proficiency in Python (or similar) for analysis and statistical work.
• The ability to translate ambiguous business problems into clear analytical questions and actionable insights.
• Confidence communicating data-driven insights to both technical and non-technical stakeholders.
• Comfort operating in fast-moving, evolving environments and adapting your approach as context changes.
• An interest in crypto and financial products, and curiosity to learn more about the space.

Life at Luno:

• Remote but reachable work policy gives you the freedom to choose between working from home or the office.
• Plus the option to buy and sell up to 5 days leave
• Improve body and mind, with excellent private medical insurance
• Access to Learnably and our additional learning platforms for your personal and professional development
• 6 months primary care-giver leave
• Paw-ternity leave for your furry friend
• Annual Inspiration Day in addition to your annual leave which increases based on your length of service! 
• A collaborative, friendly work community, with regular social events and virtual cooking, dancing, drawing and house planting classes hosted by our Lunauts
• 0 fees up to a certain amount with Luno from the day you start.*

*Offer only available on Luno Instant Buy, not Luno Exchange

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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