We're looking for a Data Scientist to help improve the quality, organization, and usability of the data that powers our AI/ML products. This isn't a reporting or dashboarding role — you'll be training, updating, and running models directly against large-scale datasets, diagnosing systemic quality issues, and building the frameworks that determine what data is fit for training. You'll work at the intersection of applied machine learning, data quality engineering, and analytical tooling.
What You'll Do
• Assess and structure training data: Evaluate large-scale datasets for quality, apply structured labeling frameworks, and diagnose systemic gaps or inconsistencies that affect model performance
• Train, update, and apply models against existing data: Train or fine-tune models, apply scoring/classification tools against datasets to auto-tier or flag quality at scale, and iterate on models as new data or requirements emerge
• Surface insights: Explore labeling and usage patterns to find where data quality is strong or weak, and turn findings into clear, defensible recommendations
• Support data pipeline integration: Partner with engineers to ensure clean, well-labeled data flows smoothly into downstream systems and model training workflows
• Test and validate: Validate model outputs and quality-scoring results against ground truth before they're used downstream, and monitor for drift or regressions as data and models evolve
• Contribute to tooling improvements: Provide input on internal tools used for data labeling and review, helping shape what they need to track, automate, or support
• Build quality frameworks: Define metrics and benchmarks that answer “how good is our data?” and track them over time, with a direct eye toward how data quality impacts model performance
What We're Looking For
• Master's degree in Data Science, Statistics, Computer Science, or a related quantitative field
• 1–4 years of experience in data science, machine learning, or a related quantitative field
• Solid understanding of machine learning fundamentals — how training data quality affects model performance, common failure modes from bad/biased data, and how to evaluate a model's output critically
• Hands-on experience training, fine-tuning, or updating ML models — not just applying pre-built tools or doing descriptive analysis
• Strong SQL and Python skills (pandas, numpy, scikit-learn, or a deep learning framework such as PyTorch/TensorFlow); comfortable exploring and cleaning large, messy datasets independently
• Practical experience working with AI coding tools (e.g., Copilot, Cursor, Claude Code, or similar) as part of your daily workflow, with an agentic mindset — comfortable delegating well-scoped subtasks to AI tools/agents, verifying their output, and iterating quickly rather than hand-coding everything from scratch
• Ability to translate ambiguous questions into measurable, trackable metrics
• Familiarity with data labeling or annotation workflows is a plus, not required
• A quality-first, curious mindset — interested in why data issues occur, not just flagging them
• Exposure to computer vision or video/image datasets specifically is a plus
Success Looks Like
• Measurable improvement in data quality and coverage over time
• Clear, trusted quality benchmarks that inform data and modeling decisions
• Strong collaboration with engineering on data pipeline health
Base Salary range of $121,000 - $150,000 DOE + bonus target up to 20% + equity