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 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.

Your Role at impact.com:

As a Senior Software Engineer, Tech Lead for Data Systems, you will lead a squad of engineers focused on designing, building, and maintaining data systems that are maintainable, reliable, and understandable at scale. You will set technical direction, own squad-level delivery outcomes, and manage the health and growth of your team - all while remaining a strong hands-on engineering contributor.

This role is centered on designing resilient distributed systems, including handling retries, partial failures, backpressure, and consistency tradeoffs. You will set the bar for clear interfaces, strong tests, and solid observability (metrics and tracing) so our systems are easy to run, debug, and evolve - and hold the team to that bar consistently.

You are expected to function as the primary technical and people leader for your squad: breaking down complex problems into well-defined units of work, driving planning and cross-team alignment, governing releases and stability, and developing your direct reports through coaching and feedback.

This is a software engineering leadership role, not a data analysis/statistical role or an ETL/SQL-focused role.

Essential Responsibilities

• Own squad-level delivery, technical direction, and outcomes (quality, stability, deadlines) across multiple workstreams
• Provide direct people management: coaching, performance feedback, career development, delegation, hiring input, and team health
• Set and align technical approach for the squad; make or drive key decisions, resolve cross-engineer disagreements, and ensure designs fit architecture and guidelines
• Drive coordination to meet quarterly objectives - work breakdown, sequencing, dependency management, and alignment with stakeholders across a distributed team
• Serve as the final accountable approver for PRs, release readiness, point releases, and build/stage stability; ensure operational discipline is followed
• Own timely incident response patterns, drive follow-up, ensure preventative actions happen (not just fixes), and improve processes to reduce recurrence
• Set engineering standards for the squad (documentation expectations, review rigor, testing discipline, quality bars) and ensure consistency
• Break down complex data logic into small, testable units and model this practice for the team
• Instrument systems with strong observability - metrics and tracing - to support debugging and operational confidence
• Develop code both independently and while pair programming

This is intended to describe the general nature and level of work being performed by people assigned to this job. It is not intended to be an exhaustive list of all responsibilities, skills, efforts, or working conditions associated with the job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Required Qualifications

Education & Experience

• Completed B.S. in Computer Science, Engineering, or related field, or equivalent professional experience
• 4+ years of Software Development experience, including experience leading or tech-leading a team

Technical Skills (Non-Negotiable)

• Comfort and proficiency in a typed language such as Java, Scala, or Kotlin
• Strong software engineering habits: modularity, testing, abstraction
• Proven ability to set and evolve technical direction for a squad or workstream
• Knowledge of software design patterns and the ability to select and implement the right pattern for a given problem
• Experience with Test-Driven Development (TDD) and building meaningful test suites
• Familiarity with Continuous Integration / Delivery methods, tooling, and integrations

Competencies

• Strong analytical thinking and problem-solving skills
• People leadership - ability to coach, give feedback, and develop engineers at varying levels
• Ability to drive planning, sequencing, and cross-team alignment across a distributed team
• Attention to detail and commitment to raising and maintaining quality standards for the team
• Critical thinking and creative problem-solving
• Ability to communicate clearly and show engagement in both remote and in-person meetings; comfortable presenting technical direction to stakeholders

Work Requirements

• Must be willing to work 2 days per week in office for collaboration with the team
• Currently based locally in Cape Town or willing to relocate

Nice-to-Have Qualifications

• Curiosity about distributed data systems (Spark experience is a plus, but not required)
• Experience breaking down complex data logic into small, testable units
• Exposure to any Google Cloud technology
• Experience with large-scale data processing systems
• Experience with monitoring, alerting, and production support
• Open-source contributions

What We're Looking For in a Teammate

• Curious - asks good questions, seeks to understand the "why" behind decisions
• Flexible - willing to change direction when priorities shift
• Hungry - a self-starter who takes initiative and owns their work and their team's outcomes
• Humble - open to feedback, eager to learn and grow, and models this for their reports
• Communicative - shows engagement and clarity in both remote and in-person settings
• Accountable - takes ownership of commitments and outcomes at both the individual and squad level

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.

Please submit your resume in English.

To learn more about our team and office culture in Cape Town, South Africa visit the following links. 
Careers Page: https://www.knowbe4.com/careers/locations/cape-town
Glassdoor: https://www.glassdoor.com/Location/KnowBe4-Cape-Town-Location-EI_IE969384.0,7_IL.8,17_IC2653748.htm
LinkedIn: https://www.linkedin.com/company/knowbe4/life/southafrica/

We are looking for a Machine Learning Engineer to help scale the industrialization of our ML lifecycle. This is a Builder’s Role - you will be responsible for the high-scale architecture that allows our models to survive production. You’ll be a key contributor to our MLOps ecosystem, moving beyond experimentation to build high-throughput, distributed systems that serve as the backbone of our intelligence products.

Machine Learning Engineers help us extract value from our data. They are involved in all the processes from data collection, cleaning, and preprocessing, to training models and deploying them to production. This position will assess, analyze, and organize data, execute tests, and optimize the learning process to develop high-performance machine learning models.

Responsibilities:

• Develops software using the KnowBe4 Software Development Lifecycle and Agile Methodologies
• Designs, develops, and researches Machine Learning systems
• Transforms data science prototypes by applying appropriate Machine Learning algorithms and tools
• Performs statistical analysis and using results to improve models
• Inference Engineering: Drive the deployment and optimization of both standard predictive models and LLM architectures, balancing trade-offs between low latency, high throughput, and cost-efficiency
• Platform Hardening: Transition research prototypes into resilient, production-ready microservices that can handle massive traffic
• Lifecycle Orchestration: Execute automated pipelines for data and model versioning, validation, and retraining
• Observability: Implement advanced monitoring for model drift, data integrity, and system health to ensure production reliability
• Collaborative Standards: Uphold clean code practices, thorough documentation, and participate in rigorous code reviews across the ML and Engineering teams

Requirements:

• BS or equivalent plus 3 years experience
• MS/Ph.D. or equivalent plus no experience
• Training in secure coding practices (preferred)
• AI/ML and Core: Python (production-grade), PyTorch
• Data and Features: Apache Spark for distributed processing; experience with Feature Stores or automated feature engineering is a plus
• Infrastructure: AWS (SageMaker, Lambda), Docker, and Terraform/IaC for environment reproducibility
• Specialized Tooling: Experience with custom inference optimization (Python-based); orchestration via lean, custom AWS and Python-based solutions using Lambda and MLflow
•  Additional: C# and JavaScript (beneficial)
• Familiarity with secure coding practices

The Reality Check:

While the title says pure ML, the day-to-day is data heavy and is also focused on the AI and ML Platform Engineering around our services. Expect a roughly 90/10 split - the vast majority of your impact will come from hardening data pipelines, refining CI/CD for ML, and building world-class observability, with the remaining portion dedicated to model building, implementation and tuning.

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.