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New York, New York, United States
Hybrid
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.
What You'll Do:
Core Responsibilities
Design, build, and evaluate recommendation models that operate across heterogeneous entities—advertisers, publishers, creators, products, and consumers—and the relationships between them. Frame problems in terms of the partnership graph and apply techniques appropriate to each surface, including candidate generation, ranking, reranking, and personalization.
Contribute to evolving our architecture toward graph-based approaches: learn semantic embeddings of entities and relationships, apply graph neural networks or attention aware graph transformer models where they add value, and build representations that generalize across surfaces and use cases. Stay current with cutting-edge techniques in graph ML, representation learning, and modern recommender architectures, and bring relevant ideas into the platform.
Build models and pipelines that serve recommendations in both batch and real-time contexts. Partner with Engineering on retrieval infrastructure, vector search, feature stores, and low-latency serving patterns. Make pragmatic tradeoffs between model sophistication, latency, cost, and freshness based on the surface and use case.
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.
Design offline evaluation (offline replay, counterfactual evaluation, holdout sets) and online experiments (A/B tests, holdouts, interleaving) to quantify model impact. Apply appropriate statistical methods, recognize common pitfalls in recommender evaluation (position bias, feedback loops, selection effects), and translate results into clear recommendations for product and engineering partners.
Work closely with Product, Engineering, and Business Stakeholders to translate platform goals into measurable model outcomes. Communicate findings, tradeoffs, and recommendations clearly to both technical and non-technical audiences. Document your work so that models, features, and decisions are understandable and reproducible by others.
What You Bring:
Salary Range: $100,000 - $125,000 per year, plus an additional 5% variable annual bonus contingent on Company performance and eligible to receive a Restricted Stock Unit (RSU) grant.
*This is the pay range the Company believes is equitable for this position at the time of this posting. Consistent with applicable law, compensation will be determined based on the skills, qualifications, and experience of the applicant along with the requirements of the position, and the Company reserves the right to modify this pay range at any time.
Benefits and Perks:
At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a benefits package that supports your well-being, growth, and work-life balance.
impact.com is proud to be an equal-opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.
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