Data Scientist
RemoteUnited States
United StatesRemoteFull TimeDoctorate Or Professional DegreeSmall
Full TimeDoctorate Or Professional DegreeSmall
Job Summary
Build production-grade machine learning models and data products ranging from generative systems to subscriber-behavior predictions for TV providers. Dig into large, messy datasets to identify trends, add functions to the core Python data science library, and execute Antenna R&D to answer real business questions. Develop LLM-powered pipelines and agents with rigorous evals, using tracing tools to validate responses, track latency, and enforce guardrails against failure modes. Debug complex distributed systems and optimize code for scale while collaborating with cross-functional stakeholders to explain technical design decisions.
Required Qualifications
- 2+ years of experience building machine learning models and data products in Python, with the engineering skills to take them from prototype to production
- Expert in Python with strong object-oriented design, software system design, and experience building high-quality, testable, production-grade code
- Hands-on experience with deep learning frameworks (PyTorch or TensorFlow), plus a deep understanding of machine learning concepts, the end-to-end model development lifecycle, and MLOps principles
- Hands-on experience with large-scale data processing tools (e.g., Apache Spark/PySpark, Dask) and strong SQL skills working with large, complex datasets
- Solid experience with cloud platforms (GCP highly preferred), including deploying, managing, and scaling services (Docker, Cloud Run, GKE) and working with big data systems (Dataproc, BigQuery)
- Excellent problem-solver, skilled at debugging complex distributed systems and optimizing them for performance and scale
- Advanced English proficiency (B2–C1) with strong communication, teamwork, and consulting skills; able to clearly explain complex technical and system design decisions
- Daily use of agentic coding tools (Claude Code, Cursor, or Codex CLI): plan first, write tests and instructions up front, and review every change before accepting it. You can still design, debug, and defend your own work without AI assistance — our interview process tests this directly
- Experience building and shipping LLM-powered agents or pipelines using an orchestration framework (LangGraph, Pydantic AI, or OpenAI Agents SDK), including custom tool definitions against internal APIs and data systems, agent state and memory, and human review steps. You can explain the failure modes you hit and the guardrails you added
- You treat evals as a core deliverable: you validate model responses with provider-enforced structured outputs (Pydantic), build eval sets with clear pass/fail checks, calibrate LLM-as-a-judge rubrics, and use tracing tools (Langfuse, LangSmith, or Braintrust) to track cost, latency, and quality over time. You can describe an eval that caught a problem a human review missed
Desired Qualifications
- Experience in or passion for the Subscription Economy, especially media and entertainment; experience working with media data or data clean rooms is a plus
- Experience with synthetic data generation or advanced generative models (e.g., GANs, VAEs, CTGAN)
- Experience building Python libraries that others use, or contributions to open-source projects
- Knowledge of advanced MLOps practices (like model monitoring) and build automation tools (e.g., Cloud Build, Cloud Run)
- Experience building custom tool integrations for agents: your own tool definitions and routing against internal APIs and data systems, with clear input schemas, validation, and safe handling of side-effecting actions
- Experience with advanced evaluation and observability practices like multi-judge calibration, automated regression suites, and production monitoring of agent quality
- Familiarity with RAG and context engineering for grounding model responses in proprietary data
- Experience using LLMs for testing pipelines and QA workflows
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