Data Scientist, Next Gen Recommendation Systems
AI summary of the role
Build next-generation recommendation systems for a partnership automation platform, working across heterogeneous entities (advertisers, publishers, creators, products, consumers) using graph-based architectures and semantic embeddings.
What you’ll do
- Design, build, and evaluate recommendation models across heterogeneous entities using candidate generation, ranking, reranking, and personalization.
- Evolve the recommender stack toward graph-based approaches with semantic embeddings, graph neural networks, and attention-aware graph transformers.
- Build batch and real-time serving pipelines, partnering with Engineering on retrieval infrastructure, vector search, and feature stores.
- Own the full ML lifecycle: data/feature design, model development, evaluation, launch, monitoring, and iteration.
What you’ll bring
- 3+ years in data science / applied ML with production models delivering measurable impact.
- Strong Python and SQL; experience with large-scale data and distributed compute (Spark/Databricks).
- Hands-on experience building recommendation or ranking systems (candidate generation, learning-to-rank, retrieval/reranking, implicit feedback).
- Experience with embeddings and representation learning for users, items, content, or entities.
Technologies
Python · SQL · Spark · Databricks · graph neural networks · PyTorch · TensorFlow · PyTorch Geometric · DGL · FAISS · ScaNN · Vespa
About impact.com
Commerce partnership platform helping brands manage affiliate, creator, referral, publisher, and B2B partner programs with tracking, contracting, payments, and analytics.
Private Late · 1000–2000 people
Source and classification
Internal deployment & tooling · Evidence for this classification:
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
More from the job description
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 [... source excerpt omitted ...] 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. What You'll Do: Core Responsibilities Multi-entity recommen [... source excerpt omitted ...] se. End-to-end ML delivery & ML engineering Own the full lifecycle of your work: data and feature design, model development, evaluation, launch, monitoring, and iteration. Build production-grade pipelines, write code that other engineers can extend, and partner with MLOps on reproducibility, observability, and reliability. Use AI coding agents aggressively to accelerate prototyping, refactoring, debugging, and shipping—we expect this to be a core part of how you work, not an occasional aid. Experimentation & measurement Design offline evaluation (offline replay, counterfactual evaluation, holdout sets) and online experiments (A/B tests, holdouts, interleaving) to quantify model
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