Data Science Partner II

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, and Fanatics - rely on impact.com to power more than 350,000 partnerships that deliver measurable business results. Your Role at impact.com: We are looking for a Senior Data Scientist to contribute to enhancing our measurement capabilities across Impact’s dynamic partnership marketing ecosystem. In this role, you will lead modeling research across a variety of measurement initiatives—including, but not limited to, attribution, identity resolution, and tracking quality—while simultaneously owning the pipelines supporting end-to-end measurement and incrementality testing. This is a high-impact individual contributor role. You will drive the measurement strategy for key measurement initiatives, designing the experiments to quantify the expected lift of attribution-enhancing projects and the robustness of our underlying identity graph and tracking pipelines. You will act as the bridge between technical integrity and business value for our measurement outcomes. You'll also serve as Impact's technical counterpart with external identity and measurement partners. You will design and run structured POCs, evaluate vendor solutions rigorously, and produce data-driven recommendations on build-vs-buy decisions that shape the long-term identity strategy. What You'll Do: Innovate & Modernize : Analyze current measurement solutions to identify systemic weaknesses and opportunities for improvement. Use cutting-edge methods in ML, graph networks, and AI to develop next-generation approaches. Pipeline Development & Evaluation: Develop robust testing environments to evaluate new models, independently or in partnership with Data Engineering. Build proactive monitoring and SLOs for system health. Own deployment, testing, and monitoring. Impact : Implement incremental enhancements to existing processes. Ensure that changes to our algorithms produce verifiable, measurable improvement in business metrics. Measure impact rigorously and communicate results to stakeholders. Vendor Strategy: Serve as the technical owner for third-party identity partner evaluations, designing structured POCs that move beyond "demo-ware" to stress-test claims against real data. Stakeholder Communication: Translate complex findings into actionable narratives for Product, Engineering, and leadership. Strategic Initiatives : Use technical expertise to assist in driving the technical measurement strategy for high-priority projects in collaboration with other teams and leadership. What You Bring: Experience: 5+ years of total experience, with significant, hands-on expertise in data-driven research and development. Attribution & Measurement: Proven track record in designing incrementality experiments, A/B testing, and assessing the impact of new initiatives. Engineering Rigor: Proven ability to build production-grade pipelines. Fluent in Python/SQL and comfortable owning code from research through to production deployment. Analytical Communication: Ability to articulate complex tradeoffs (e.g., graph accuracy vs. latency or incrementality vs. scale) to stakeholders who need to make build-vs-buy decisions. Education: Bachelor's in a quantitative field (CS, Statistics, Math, Engineering, or similar); Master's/PhD preferred, or equivalent work experience Nice To Have: Familiarity with ML-based entity resolution approaches: blocking strategies, embedding-based similarity, probabilistic matching, and hierarchical clustering. Experience with graph databases and graph analytics (Neo4j, Spanner, PuppyGraph, NetworkX, or similar) applied to identity or entity data. Deep understanding of multi-touch attribution (MTA) and cross-channel measurement. Experience with device fingerprinting, cross-device identity, or cookie-less identity resolution approaches. Experience evaluating third-party identity resolution or data enrichment vendors; familiarity with the external identity and measurement partner landscape. Exposure to affiliate marketing, ad tech, or performance marketing data environments and the identity challenges specific to those ecosystems. Familiarity with GCP tools (BigQuery, Vertex AI, Dataflow, Cloud Run) and/or Databricks/Spark for large-scale data processing. Knowledge of privacy-preserving identity techniques (differential privacy, hashing, clean rooms) and their implications for measurement. What Sets You Apart: Clear under complexity. You can articulate what our measurement systems represent, where they break down, and what fixing them is worth, whether to an engineer, a product manager, or a senior leader. Ownership mindset. You treat our measurement systems like they’re yours. You don't wait to be asked to find the next problem; you're already looking. Innate curiosity. You stay current on the external landscape for identity resolution, data enrichment, and measurement vendors. You bring relevant developments to the team proactively. Detective instincts. You love hunting bad data. You don't accept anomalies at face value: you dig until you understand the root cause, whether it's a pipeline bug, a vendor data issue, or a fundamental schema flaw. Research-to-production fluency. You move comfortably between prototyping new

This role involves applying advanced data science techniques to support research and measurement products. It requires expertise in machine learning and statistical modeling to derive insights from large datasets.

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 P

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