Staff Data Scientist

Responsibilities

• Deeply understand customer, marketplace, and business problems through data and translate into ML objectives, features, models, and measurement plans.
• Deliver and deploy end-to-end machine learning models, from problem framing and feature engineering through model development, experimentation, launch, monitoring, and iteration.
• Build zero-to-one models in areas powered by heuristics or business rules, and improve existing production models across demand, supply, ranking, personalization, renter intent, and marketplace optimization.
• Apply statistical mindset to model development, experimentation, causal inference, tradeoff analysis, and decision-making.
• Lead ambiguous, high-leverage technical work: define scope, evaluate approaches, manage tradeoffs, and align stakeholders.
• Partner closely with Product, Engineering, Design, Analytics, Marketing, GTM and Growth to build ML systems that drive value.
• Communicate ML opportunities, tradeoffs, and results to technical and non-technical audiences.
• Mentor and collaborate with other data scientists to raise modeling, experimentation, and analytical practice.
• Leverage modern AI tools to improve productivity across coding, analysis, documentation, and workflow automation.

Requirements

• 7+ years of industry experience developing, deploying, and iterating on machine learning models in production.
• Degree in Computer Science, Computer Engineering, Mathematics, Statistics, Economics, Physics, or related quantitative field.
• Deep proficiency in Python and SQL, comfort with full model development lifecycle.
• Familiarity with standard ML libraries and frameworks such as scikit-learn, XGBoost, TensorFlow, PyTorch, or similar.
• Experience with cloud platforms; GCP preferred.
• Broad set of statistical and machine learning methods to solve business problems.
• Strong grounding in statistical learning, modeling, experimental design and analysis, and causal inference.
• Ability to work through feature engineering, feature selection, hyperparameter tuning, model evaluation, and optimization.
• Ability to quantitatively research opportunities, define technical strategy, set scope, manage timelines, and drive outcomes.
• Comfort communicating and collaborating cross-functionally.

Nice-to-Haves

• Experience optimizing within a two-sided marketplace or multi-stakeholder environment.
• Background in recommendation systems, ranking, personalization, search, or matching.
• Experience with performance marketing models, paid acquisition, supply-side optimization, or marketplace incentives.
• Familiarity with MLOps practices, ML engineering workflows, model monitoring, Airflow, dbt, or similar.
• Master's degree or PhD in relevant quantitative field.

Compensation

• US base salary range: Zone 1: $203,000 - $238,000; Zone 2: $188,000 - $220,000; Zone 3: $172,000 - $202,000 + equity.