Staff Data Scientist - Acquisition

• Own causal measurement for paid search: Stand up uplift/incrementality frameworks (e.g., doubly robust learners, causal forests, DML, IVs, synthetic control, DiD, BSTS) to quantify lift beyond correlation.
• Ship production models: Build and serve models that inform bids, budgets, and query-level targeting using signals like incremental CPA, tROAS, LTV, and heterogenous treatment effects.
• Design experiments & guardrails: Architect geo/cell tests and online experiments; handle power analysis, pre-trend checks, SUTVA threats, SRM detection, and sequential monitoring.
• Integrate with ad platforms: Translate science into APIs/feeds for Google Ads, Microsoft Advertising, and SA360; validate against auction dynamics and Quality Score mechanics.
• Data & MLOps leadership: Partner with platform teams to instrument events, build reliable feature stores and ETL (batch/stream), and establish monitoring for drift, bias, leakage, and attribution sanity.
• Mentor & influence: Provide technical leadership across science, engineering, and marketing; set standards for methodology, code quality, documentation, and reproducibility.
• Tell the story: Communicate trade-offs and impact to execs and non-technical partners; make the complex understandable and actionable.

What You've Done:

• 8+ years in applied ML/causal inference (or equivalent) with direct paid search/auction experience.
• Expert in causal methods (uplift modeling, DML, IV, DiD/synth control, BSTS/Bayesian time series) and experimental design.
• Strong software engineering: Python (pandas, numpy, scikit-learn, LightGBM/XGBoost), SQL; experience with Spark and one of AWS/GCP/Azure.
• Hands-on with A/B frameworks, power analysis, and measurement diagnostics (SRM, balance, interference).
• Proven track record integrating with Google Ads/Microsoft Ads/SA360 and moving the needle on tROAS, CPA, LTV.
• Clear communicator who can mentor senior ICs and partner with product/marketing.

Nice to Have:

• Strong experience with SEM optimization and bidding, particularly from the ad-buyer side.
• Recsys, bandits/RL for bidding/budget pacing, MMM and privacy-aware attribution.
• Scala/Java or microservices experience; Airflow/DBT; Kafka/PubSub; Feast or similar feature stores.
• Domain knowledge of auction theory, query taxonomy, brand vs. non-brand dynamics, and budget rebalancing.

Staff-Level Capabilities

• Technical leadership through influence rather than formal management authority
• Strategic thinking with the ability to balance long-term technical vision with immediate organizational needs
• Cross-functional collaboration skills to work effectively with Data Science, Product, and Engineering teams
• Communication skills to inject technical context into high-level organizational discussions
• Problem-solving approach for ambiguous, high-impact technical challenges
• Mentorship and sponsorship experience growing junior and mid-level engineers