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.

At 2U, we are all in on purpose. We are motivated by our mission – to eliminate the back row in education – and connected by our shared passion to deliver world-class digital education at scale. As the parent company of edX, the world’s leading online learning platform, 2U powers more than 4,000 online higher education offerings – from free courses to full degrees. Together with more than 230 colleges, universities, and corporate partners, we are helping to unlock human potential.

What We’re Looking For:

The Data Scientist III will technically lead analytical research, machine learning and other data science initiatives across all lines of business, including degree, executive education, and open course programs. The Data Scientist III would be expected to engage with stakeholders, lead technical solution design and delivery. This is a highly quantitative role that requires analytical aptitude, strong programming skills, and intellectual curiosity. We are looking for someone who is excited about solving business problems, eager to learn and apply experimental and machine learning techniques, and motivated to develop their skills in a collaborative team environment.

Responsibilities Include, But Are Not Limited To:

• Lead statistical analysis, reporting and visualization to support business decision-making across degree, executive education, and open course lines of business. 
• Lead the end to end technical delivery of Data Science projects/models  over use cases that span all lines of business and functional areas, identifying high value opportunities and implementing the solution. 
• Lead design and implementation of A/B tests on Data Science projects/models implemented to determine and size success of solutions. 
• Collaborate with engineers and ML engineers to support productionization efforts, including code development, testing, and integration into the 2U software ecosystem.
• Partner with stakeholders and product to gain domain knowledge, understand requirements, and contribute to planning development tasks and projects.

Things That Should Be In Your Background:

• Bachelor's degree in data science, statistics, mathematics, computer science, economics or other quantitative field required
• 5-10 years of experience in data science or related role
• Proven track record of delivering production-grade machine learning models and data science products that drove significant business impact
• Demonstrated experience designing and analyzing online controlled experiments (A/B testing)
• Proven experience applying analytical techniques such as classification, regression, cluster analysis, pattern recognition, and predictive modeling 
• Strong proficiency in Python and other object-oriented programming languages (R, Java, Scala, or similar)
• Advanced SQL skills and experience querying relational and/or NoSQL databases
• Solid understanding of applied statistics
• Experience with data visualization and business intelligence tools (e.g., Tableau, Looker, PowerBI, Domo, or similar platforms) 
• Familiarity with data engineering concepts and ETL processes 
• Excellent communication skills and ability to work collaboratively in a team environment while receiving moderate guidance and direction 

Other Attributes That Will Help You In This Role:

• Graduate degree or additional coursework in quantitative fields is a plus

Benefits & Culture

Our global employee base is a diverse collection of innovators, dreamers, and doers working together to transform lives through higher education. We believe that every employee can advance our shared purpose, and that life at 2U should be fun and meaningful. If you’re excited by the opportunity to provide over 40 million learners and counting with access to world-class online higher education, then join us – and do work that makes a difference. #NoBackRow

We offer comprehensive benefits (unique per country) and excellent work/life balance.
Full-time, ZA benefits include: 

• 2 complimentary Getsmarter short courses per year
• Subsidised medical aid with Discovery Health Medical Scheme
• 4% 2U contribution towards Discovery Life Pension Fund and Group Risk Benefit
• Employee Assistance Program (EAP)
• Generous leave policy including time off to volunteer for non-profit organization, study leave, sports leave and a company-wide festive season break

2U Diversity and Inclusion Statement

At 2U, we are committed to building and sustaining a culture of belonging, respect, and inclusion. We are proud of the steps we’ve taken to bring together an employee base that embodies diverse walks of life, ideas, genders, ages, races, cultures, sexual orientations, abilities and other unique qualities. We strive to offer a workplace where every employee feels empowered by what makes us different, as well as by how we are alike. 

2U is committed to providing reasonable accommodations during our recruitment process. If you need assistance or accommodations, please reach out to us at: recruitingaccommodations@2u.com. 

About 2U

For more than a decade, 2U has been the digital transformation partner of choice to great non-profit colleges and universities delivering high-quality online education at scale. As the parent company of edX, a leading global online learning platform, 2U provides over 45 million learners with access to world-class education in partnership with more than 230 colleges, universities, and corporations. Our people and technology are powering more than 4,000 digital education offerings — from free courses to full degrees — and helping unlock human potential. To learn more: visit 2U.com.

About edX

edX is the education movement for restless learners and a leading global online learning platform from 2U. Together with the majority of the world’s top-ranked universities and industry-leading companies, we bring our community of over 45 million learners world-class education to support them at every stage of their lives and careers, from free courses to full degrees. And we're not stopping there — we're relentlessly pursuing our vision of a world where every learner can access education to unlock their potential, without the barriers of cost or location. Learn more at edX.org.

Learn more at https://2u.com/careers/
#NoBackRow

The above statements are intended to describe the general nature and level of work performed by individuals assigned to this position, and are not intended to be construed as an exhaustive list of all responsibilities, duties and skills required. All employees may be required to perform duties outside of their normal responsibilities from time to time, as needed.

2U is an equal opportunity employer that does not discriminate against applicants or employees and ensures equal employment opportunity for all persons regardless of their race, creed, color, religion, sex, sexual orientation, gender identity, pregnancy, national origin, age, marital status, disability, citizenship, military or veterans’ status, or any other classifications protected by applicable federal, state or local laws. 2U’s equal opportunity policy applies to all terms and conditions of employment, including but not limited to recruiting, hiring, training, promotion, job benefits and pay. 

2U is strongly committed to diversity within its community and especially welcomes applications from South African citizens who are members of designated groups who may contribute to Employment Equity within the workplace and the further diversification of ideas. In this regard, the relevant laws and principles associated with Employment Equity will be considered when appointing potential candidates. We are required by law to verify your ability to work lawfully in South Africa. 2U requires that you submit a copy of either your identity document or your passport and any applicable work permit if you are a foreign national, along with an updated curriculum vitae.

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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