Data Scientist, Next Gen Recommendation Systems

Responsibilities

• Core Responsibilities
• Multi-entity recommendations across the partnership graph
• Graph-based modeling & semantic embeddings
• Batch and real-time serving
• End-to-end ML delivery & ML engineering
• Experimentation & measurement
• Cross-functional collaboration
• What You Bring:
• 3+ years of experience

Additional Information

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 350,000 partnerships that deliver measurable business results. Your Role at impact.com : We're seeking a Data Scientist to help build the next generation of recommendation systems powering our partnership automation platform. Our ecosystem connects a rich set of entities-advertisers, media publishers, creators, products, and consumers-and the relationships between them are where the real value lives. Your work will help surface the right partnerships, the right products, and the right content across this network at scale. You'll contribute to evolving our recommender stack toward a graph-based architecture leveraging semantic embeddings of entities and their relationships , applying cutting-edge techniques in representation learning, graph ML, and retrieval. The system needs to serve recommendations both in batch and real time, respond to dynamic user inputs , drive measurable value for end users across the platform, and remain reliable as the ecosystem grows. This role is hands-on and end-to-end. You'll own modeling and experimentation work for a defined area of the recommendation stack-from problem framing through productionization-in close partnership with Engineering, Product, MLOps, and Business Stakeholders. You're expected to bring (or actively develop) ML engineering chops so you can take a solution from prototype to production, and to be a relentless user of AI coding agents to multiply your output and accelerate iteration.

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