Data Scientist, Next Gen Recommendation Systems

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. 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.

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.

Requirements

3+ years of experience in data science / applied ML, with a track record of shipping production models that delivered measurable user or business impact; Strong Python and SQL skills; experience working with large-scale data and distributed compute (Spark/Databricks or equivalent); Hands-on experience building recommendation or ranking systems—candidate generation, learning-to-rank, retrieval and reranking, or implicit feedback modeling; Experience with embeddings and representation learning for users, items, content, or other entities; ML engineering capability (or strong willingness and demonstrated ability to develop it): you can build, ship, and maintain production pipelines—not just prototypes in notebooks; Strong experimentation skills: designing and analyzing A/B tests, interpreting results, and communicating findings to stakeholders; Relentless user of AI coding agents in your day-to-day workflow, with a clear sense of where they accelerate you and where they don't; Insatiable curiosity about new techniques, architectures, and tools—and a track record of teaching yourself things quickly. You read papers, try new tools, and bring ideas back to the team; Strong problem-solving instincts and the ability to operate with growing autonomy in ambiguous, evolving spaces.

Nice to have

Experience with graph-based ML: graph neural networks (KGAT, transformers or similar), graph-aware retrieval, or knowledge graph embeddings; Experience with modern deep learning recommender architectures (two-tower, sequence/transformer-based recommenders, multi-task ranking); Familiarity with vector search and vector databases (FAISS, ScaNN, Vespa, Milvus, or similar) and approximate nearest neighbor methods; Experience with real-time ML serving, feature stores (Feast, Tecton, or equivalent), and low-latency inference patterns; Exposure to contextual bandits, reinforcement learning, or off-policy evaluation in recommendation settings; Familiarity with PyTorch/TensorFlow and PyTorch Geometric / DGL for graph workloads; Familiarity with GCP (Vertex AI, BigQuery, Cloud Run) and/or mature MLOps practices (CI/CD for ML, monitoring, drift detection); Experience in adtech, martech, e-commerce, or two-sided/multi-sided marketplace recommendations.

Benefits

Medical, Dental, and Vision insurance; Office-only catered lunch every Thursday, a healthy snack bar, and great coffee; Flexible spending accounts and 401(k); Responsible PTO policy; mental health and wellness benefit up to 12 fully covered therapy/coaching sessions per year; monthly gym reimbursement; Restricted Stock Units (RSUs); 5% variable annual bonus contingent on Company performance; free Coursera subscription and PXA courses; parental leave policy with 26 weeks fully paid for primary caregiver and 13 weeks for secondary caregiver; technology stipend and monthly internet expense allowance.