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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist, Next Gen Recommendation Systems - **Company:** Impact - **Location:** New York, NY, United States - **Experience:** Starter - **Salary:** $100,000.0 - $125,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Artificial Neural Networks, Big Data, BigQuery, Software Quality, Continuous Integration, Software Debugging, Graph Database, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Standard Sql, Search Technologies, Reinforcement Learning, Pytorch, Apache Spark, Deep Learning, Machine Learning Operations, Code Restructuring, GPT, Databricks - **Published:** August 19, 2026 - **Apply:** https://job-boards.greenhouse.io/impact/jobs/8509182002 ## About the Role * 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., * 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., * You think in terms of user and business outcomes, not just model metrics, and you connect modeling choices to real value delivered through the platform. * You're rigorous about measurement but pragmatic about shipping-able to deliver MVPs and iteratively evolve them into durable production systems. * You're a relentless learner. New papers, new tools, new architectures-you find them, try them, and figure out where they fit. You don't wait to be told what to learn. * You use AI coding agents like a power user: they're a force multiplier in your hands, not a crutch. * You have engineering instincts-you care about code quality, system design, and operability, not just model performance. * You're comfortable with messy data, noisy feedback, edge cases, and ambiguous problem spaces, and you excel at iterative improvement. * You bring clarity to ambiguity, communicate clearly across functions, and help teams align on tradeoffs., How many years of professional experience do you have in Data Science or Machine Learning Engineering? * 0-2 2-4 5+ Do you have prior experience building recommendation systems? * 0-2 2-4 5+ Do you have experience with deep learning using tensorFlow or Pytorch?* ## Description 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. 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 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. Cross-functional collaboration 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. 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