Senior/Lead Data Analyst, Medal.tv

• If you love turning messy, high-volume media data into trustworthy, measurable assets—and you get excited about building feedback loops that make ML systems smarter—this is for you
• 5+ years in data analytics or data science with a focus on media or ML data quality in production systems
• Fluency in SQL and Python (Pandas/NumPy); you’re comfortable building reproducible notebooks and code-reviewed pipelines
• Strong measurement chops: you’ve defined and computed label & model quality metrics (precision/recall/F1, mAP, AUROC, calibration, temporal IoU) and can explain their trade-offs
• Data validation & ETL experience: Great Expectations/TFDV (or equivalent), dbt, and an orchestrator (Airflow/Prefect)
• Warehouse & BI: BigQuery (or similar) plus Looker/Mode/Tableau (or similar); you build clear dashboards and know when to run deep dives
• Experimentation: A/B testing design and analysis; comfort with pitfalls and guardrails
• Product sense & communication: you turn ambiguous problems into measurable roadmaps and communicate findings clearly to technical and non-technical partners
• A love for gaming, however you define it

Desirable

• We’re looking for a Senior/Lead Data Analyst to own the quality of the video data that powers Medal’s machine learning features
• You’ll partner closely with ML researchers, data engineering, and product to measure, diagnose, and improve the accuracy, completeness, and reliability of our video datasets and labels
• Own the video data quality program: define quality KPIs (coverage, precision/recall, calibration, temporal alignment, label latency, drift) and build dashboards that make them visible company-wide
• Audit datasets at scale using SQL and Python: create automated checks for codec/bitrate/fps/resolution, audio/video sync, corruption, duplicates, and long-tail coverage by game, device, and region
• Design ground-truth pipelines: human-in-the-loop reviews and labeling guidelines; measure annotator agreement, and iterate to improve label quality
• Diagnose model-data issues: collaborate with ML to localize failure modes, quantify data gaps, and prioritize data collection or relabeling to move accuracy on real user content
• Detect bias and drift across games, platforms, and cohorts; propose mitigations and monitor post-launch
• Instrument product and ingestion to capture the metadata ML needs (e.g., encoding, device, frame rate, content type) while respecting privacy and safety constraints
• Run experiments: design and analyze A/Bs and holdouts to connect data quality improvements to model and product outcomes
• Champion best practices in data contracts, validation, reproducibility, and documentation; mentor analysts and influence data quality culture
• Work on-site at our NYC office 5 days a week