data
Posted Feb 13Senior Data Scientist, Product Data
Hybrid
Responsibilities
- 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
- Build and improve matching algorithms to link sales transactions to catalog products, handling missing GPIDs, naming inconsistencies, and category misclassification.
- 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.
- Build and maintain product graph infrastructure that captures relationships between products, variants, brands, categories, retailers, and transactions.
- 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
- 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.
Requirements
- 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).
- ML & classification
- 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.
- 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
- 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.
Experience
- 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.
Benefits
- Benefits and Perks:
- 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.
- Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year , with additional dependent coverage.
- 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.
- 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
Contact
- 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.
- 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
- At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a
- impact.com is proud to be an equal opportunity workplace.
Additional details
- Its award-winning products— Performance (affiliate), Creator (influencer), and Advocate (customer referral)—unify every type of partner into one integrated platform.
- 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
- 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
- Contribute to the design and optimization of retrieval pipelines that combine text, attributes, and embeddings for search and recommendations.