Hiring: Senior Data Scientist - Experimentation & Causal Inference Company: ByLabs Location: San Francisco Bay Area Job Posted Time: 2026-09-10 13:39:11 Target Skills & Keywords : Data Pipeline, Data Warehouse, Flink, Kafka, Python, SQL, SciPy About the job Experience: •3+ years of industry experience designing and analyzing online controlled experiments at a tech company with meaningful user scale (not exclusively survey experiments or clinical trials). •Solid foundations in hypothesis testing, power analysis, multiple testing correction, and sequential testing. Hands-on experience with at least two of: variance reduction methods (CUPED or similar), sample ratio mismatch detection, or continuous data quality monitoring for experiments. •Proficient in Python (scipy, statsmodels, or equivalent) for statistical analysis and simulation. Comfortable writing complex SQL (window functions, CTEs) for data extraction and validation. •Demonstrated capacity to explain complex statistical concepts to non-technical stakeholders, translate business questions into rigorous experimental designs, and define clear specifications so engineers can implement your methods in production. •Strong AI Sense — extensive hands-on experience with AI coding tools (Claude Code, OpenClaw, or similar). Demonstrated ability to leverage AI assistants to automate repetitive analytical tasks, accelerate code development, and build self-serve tooling faster. You treat AI tools as a daily productivity multiplier, not a novelty. Required Skills: •Join a team of data scientists and platform engineers within the Big Data group, reporting to the Head of Big Data. The experimentation platform engineering team implements your specifications into production systems. •Design and maintain automated anomaly detection for live experiments — including Sample Ratio Mismatch (SRM) checks and traffic split validation. •Define alerting thresholds and circuit-breaking criteria so compromised experiments are flagged or stopped before polluting decisions. •Define and validate guardrail metrics (sensitivity, directionality, interpretability) that protect the business during every experiment. •Implement and iterate on CUPED and related pre-experiment covariate adjustment methods to reduce metric variance. •Develop techniques to remove noise from user historical behavior, enabling faster detection of true treatment effects — especially in limited-traffic or high-priority scenarios. •Shorten average experiment duration from 14 days to 9 days, unlocking 40%+ more experiments per quarter. Qualifications: •MS or PhD in Statistics, Biostatistics, Economics (Econometrics), Computer Science, or a related quantitative field. Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!