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What the market looks like

We've tracked 3,100+ mid-level data science postings across the US in the last six months, with concentrated hiring in California, New York, and Washington. Technology, financial services, and professional services firms are driving the bulk of demand. Median compensation for this cohort lands around $170,000, with top-of-band roles reaching $300,000. The strongest candidates bring hands-on experience shipping end-to-end ML pipelines, a track record translating ambiguous business problems into quantitative frameworks, and genuine fluency with production data engineering—not just notebook work.

Job responsibilities

Build and own end-to-end machine learning pipelines from data ingestion through model serving, taking accountability for production reliability and performance
Partner with product and engineering teams to translate business requirements into concrete modeling objectives and validation strategies
Design and run rigorous experiments, establishing proper baselines, control groups, and statistical rigor to support decision-making
Drive model governance and documentation practices, ensuring transparency around assumptions, limitations, and ongoing performance monitoring
Mentor junior analysts and collaborate across data, analytics, and engineering functions to integrate insights into product roadmaps
Evaluate and integrate emerging tools and techniques—LLMs, modern data platforms, AutoML—where they genuinely add value over existing approaches
Own feature engineering and data quality workflows that feed model training, balancing technical rigor with delivery velocity

Candidate requirements

5–8 years of hands-on data science, machine learning engineering, or analytics experience, with 2+ years spent deploying models into production systems
Demonstrated competence with Python, SQL, and core ML libraries (scikit-learn, XGBoost, PyTorch, or equivalent); experience instrumenting models in real codebases
Track record translating vague business questions into testable hypotheses and communicating results to non-technical stakeholders
Solid understanding of experimental design, statistical testing, and validation techniques; comfort navigating trade-offs between model complexity and interpretability
Experience working in cross-functional teams with engineers and product managers; comfort taking ownership of work that touches multiple domains
Familiarity with data pipeline orchestration, cloud data platforms (Snowflake, BigQuery, Databricks), or MLOps tooling

Senior Data Scientist