Senior Data Scientist, Product Data

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.

Responsibilities

Product classification & taxonomy modeling; Catalog & sales universe data quality; Global Product ID (GPID) coverage & matching; Deduplication & entity resolution; Manufacturer data quality & brand engagement; Product search & retrieval infrastructure; Product graph & relational modeling; Insights, monitoring & reporting; Engineering & production deployment

Requirements

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; 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

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

Benefits

Flexible Working (Responsible PTO policy); Health and Wellness (up to 12 fully covered therapy/coaching sessions per year, monthly gym reimbursement); A Stake in Our Growth (Restricted Stock Units with 3-year vesting schedule); Investing in Your Growth (free Coursera subscription and PXA courses); Parental Support (26 weeks fully paid leave for primary caregiver, 13 weeks fully paid leave for secondary caregiver); Technology Financial Support (technology stipend and monthly internet allowance)