Senior/Lead Data Analyst

The most powerful foundation models are trained on written words. But human intelligence far exceeds language. Truly intelligent machines must move from words to worlds, and acquire the capacity to perceive, anticipate, and improvise. They need to obtain a general intuition of reality.

We embrace games as the ultimate expression of ingenuity and problem solving. General Intuition builds on the strength of Medal, the world’s largest and fastest-growing platform for gaming moments. Every year, our players capture billions of gameplay clips, each representing a unique, action-packed highlight. Across countless environments, this diversity leads to uniquely capable agentic systems.

For the past year, we’ve been pushing the frontier across:

• Agents capable of deep spatial and temporal reasoning

• World models that provide training environments for those agents, and

• Video understanding with a focus on transfer beyond games

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.

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.

• 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.

• 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.

• Experience running annotation programs (Label Studio, CVAT, Scale or custom tooling) and crafting labeling taxonomies for actions/events/scenes.

• Hands-on with video tooling: ffmpeg/ffprobe for metadata & probes; familiarity with OpenCV (and running lightweight inference with PyTorch/TensorFlow for scoring/spot checks).

• Duplicate/near-duplicate detection (perceptual hashing, embeddings/FAISS/Milvus) and dataset dedup at scales

• Privacy, safety, and policy consider.ations for user-generated video (GDPR/COPPA basics, PII redaction, content safety heuristics).

• Spark/PySpark or distributed compute for heavy lifts.

• Familiarity with CV/ASR signals (scene boundary, keypoint/action recognition, speech-to-text) to enrich labels and audits.

• Prior history as a Medal user—share a clip or your profile!

• Directly shape the data foundation behind ML features used by millions of gamers.

• Work with a passionate team that values ownership, craftsmanship, and speed.

• Competitive salary, equity options, comprehensive health insurance, and 401k.

• See your work translate into more accurate models and better creator experiences—fast