Data Scientist, Gen Recommendation Systems

Position: Data Scientist, Next Gen Recommendation Systems
Location: New York

# Data Scientist, Next Gen Recommendation Systems impact.comFull Time junior New York, New York, US $100k – $125k Posted Yesterday## Role  is hiring a entry-level Data Scientist, Next Gen Recommendation Systems. This is a full-time role in New York, New York. Part of 's Brand hiring, posted yesterday. The posted range is $100k to $125k. Full responsibilities, required qualifications, and the apply link are listed in the description below.##

Salary Context

This role offers $100k-$125k. The median for Junior-level Brand roles is $60k-$70k (based on 22 listings). 73% above median.## Resume Keywords to Include Make sure these keywords appear in your resume to improve ATS scoring

PythonSQLGCPBigQueryRESTSpark Tensor Flow Py Torch Sign  up free to auto-tailor your resume with all these keywords and get a higher ATS score## Job description

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.

Your Role at '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 '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 DoCore Responsibilities Multi-entity recommendations across the partnership graph

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.

Graph-based modeling & semantic embeddings

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.

Batch and real-time serving

Build models and pipelines that serve recommendations in both batch and real-time contexts.…