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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - Performance - **Company:** VIBES LLC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Airflow, Data Infrastructure, Information Leak Prevention, Software Debugging, Python (Programming Language), Recommender Systems, Tensorflow, Pytorch, Deep Learning, ONNX (Open Neural Network Exchange) Format - **Published:** September 22, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pm2v17dwgg ## About the Role * Hands-on deep learning experience shipped in a professional environment * Strong Python skills, with PyTorch preferred (TensorFlow or JAX also fine) * Experience diagnosing model failures, including data leakage, bias, calibration, and distribution shift * Ability to build algorithms from scratch and reason about what's happening under the hood * A track record of tying modeling work to a specific business KPI you moved Nice to Haves * Experience with massive-scale datasets, including billions of impressions, events, or user records * Deep learning on tabular data and sparse user representations * Background in CTR/CVR prediction, recommendation systems, or identity graphs and cross-device attribution * Experience with production ML tooling such as ONNX export, orchestration (Dagster), or inference serving (Triton) ## Description You'll join the Performance team, the group that owns advertiser outcomes at Vibe end to end, from prediction models to bidding to the delivery stack that puts both into production. You'll report to the Head of Performance. This role exists because the modeling roadmap has outgrown current bandwidth, right as the scale and diversity of data available to the team is about to grow significantly. CTV performance advertising is still being invented: cross-device attribution and identity graphs linking TVs to households look nothing like web display or search, so there's no playbook to copy here, you'd be writing it. You'll work with data few data scientists ever touch, joining CTV signal with real-world purchase and behavioral data in ways that change how the team measures, attributes, and trains. Vibe is growing fast enough that the model architecture is still yours to shape, and established enough that your work ships to real advertisers and shows up in the numbers within weeks. What You'll Do Own the Prediction Stack * Improve the unified multi-task model predicting visits, purchases, and other outcomes simultaneously * Design shared representations so model heads learn from each other across sparse conversion funnels * Take ideas from exploration to production, covering dataset design, architecture, training, and deployment * Debug production models for gradient issues, convergence failures, drift, and regression * Partner with Performance engineers building the serving and bidding infrastructure Build the Data Foundation * Design new features from raw signal, including household-level features from the identity graph * Prepare the modeling stack to absorb new, richer data sources Drive Business Impact * Optimize models for measurable advertiser uplift instead of offline metrics that never ship * Ship changes to production, measure results, and adjust based on real outcomes ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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