Senior Data Scientist — Identity Graph Architect

About  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,  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,  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  to power more than 350,000 partnerships that deliver measurable business results.

We’re looking for a Senior Data Scientist with deep expertise in identity graph construction and resolution to lead a critical modernization of Impact’s identity graph infrastructure. This is a technically demanding individual contributor role — you’ll own the diagnostic, architectural, and implementation work required to understand our current identity graph end-to-end, identify its weaknesses, and drive meaningful, measurable improvements.

You’ll start by mapping and deeply understanding the existing pipeline, then move quickly to identify and execute near‑term wins while simultaneously building the research and testing infrastructure needed to evaluate next‑generation identity resolution approaches. A significant part of the role involves detecting, filtering, and eliminating bad data that degrades graph quality — requiring both investigative rigor and strong engineering capability.

You’ll also serve as Impact’s technical counterpart with external identity and measurement partners — designing and running structured POCs, evaluating vendor solutions rigorously, and producing data‑driven recommendations on build‑vs‑buy decisions that shape the long‑term identity strategy.

• Conduct a thorough, end‑to‑end mapping of Impact’s current identity graph construction pipeline — data sources, entity linking logic, resolution rules, graph schema, and downstream consumers.
• Document the architecture clearly and completely, creating a shared foundation of understanding for Data Science, Engineering, and Product stakeholders.
• Identify structural weaknesses, coverage gaps, resolution failures, and data quality issues that degrade graph accuracy and completeness.
• Produce a prioritized inventory of problems and opportunities, distinguishing quick wins from longer‑horizon architectural improvements.

• Identify and execute high‑leverage, near‑term improvements to the identity graph — targeting resolution accuracy, coverage, and data freshness without requiring full architectural overhaul.
• Implement incremental enhancements to existing matching and linking logic; measure impact rigorously and communicate results to stakeholders.
• Build the habit of continuous improvement into the graph pipeline: monitoring, alerting, and iterative refinement as ongoing practice rather than one‑time effort.

• Research, design, and implement methods to systematically identify, filter, and eliminate bad data in the identity graph — including corrupted identifiers, ghost entities, erroneous links, and stale or conflicting records.
• Build detection pipelines that surface data quality issues proactively; define quality thresholds and SLOs for graph health.
• Establish feedback loops that catch new bad data patterns as they emerge, preventing quality degradation over time.

• Develop a robust testing and experimentation environment for evaluating cutting‑edge identity resolution techniques — including probabilistic matching, deterministic linking, graph‑based entity resolution, and ML‑based approaches.
• Design evaluation frameworks with clear, reproducible metrics for resolution precision,…