Data Scientist II, ML Infrastructure

Stack

Responsibilities

• ▹Turn research-grade DS workflows (e.g. proxy metrics, staleness models) into production ML pipelines using Airflow, WandB and Ray, with reusable patterns
• ▹Apply and productionize causal inference methods (propensity scoring, IPW, TMLE) on the production ML stack
• ▹Build self-serve tooling so non-experts can derive rigorous causal insights
• ▹Partner with ML engineers and product teams on better tooling, metrics and measurement methods
• ▹Build data-driven frameworks from feature importance to content deindexing
• ▹Design and build centralized ML platform tooling for feature and model creation, evaluation and trust

Requirements

• ▹2+ years of hands-on experience as an applied scientist, ML engineer, research scientist or software engineer, with significant ML production experience
• ▹Strong Python skills
• ▹Experience with PyTorch or an equivalent deep learning framework
• ▹Familiarity with distributed compute (Spark, Ray)

Soft skills

About the company

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