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 5000 global brands including Walmart Uber Shopify Lenovo L’Oréal and Fanatics rely on impact.com to power more than 225000 partnerships that deliver measurable business results.
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.
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.
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 /
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) 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.
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
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
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.
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