Staff Data Scientist

Responsibilities

• Understand customer, marketplace, and business problems through data and translate them into ML objectives, features, models, and measurement plans.
• Deliver and deploy end-to-end machine learning models, from problem framing and feature engineering through development, experimentation, launch, monitoring, and iteration.
• Build zero-to-one models and improve production models across demand, supply, ranking, personalization, renter intent, and marketplace optimization.
• Apply statistical methods to model development, experimentation, causal inference, tradeoff analysis, and decision-making.
• Lead ambiguous, high-leverage technical work by defining scope, evaluating approaches, managing tradeoffs, and aligning stakeholders.
• Partner with Product, Engineering, Design, Analytics, Marketing, GTM, and Growth.
• Communicate ML opportunities, tradeoffs, and results to technical and non-technical audiences, including senior stakeholders.
• Mentor and collaborate with data scientists.
• Leverage modern AI tools to improve coding, analysis, documentation, and workflow automation.

Requirements

• 7+ years of industry experience, or equivalent experience, developing, deploying, and iterating on production machine learning models.
• Degree in Computer Science, Computer Engineering, Mathematics, Statistics, Economics, Physics, or a related quantitative field.
• Deep proficiency in Python and SQL across the model development lifecycle.
• Familiarity with standard ML libraries and frameworks such as scikit-learn, XGBoost, TensorFlow, PyTorch, or similar tools.
• Experience with cloud platforms; GCP experience is preferred but not required.
• Broad knowledge of statistical and machine learning methods.
• Strong grounding in statistical learning, modeling, experimental design and analysis, and causal inference.
• Experience with feature engineering, feature selection, hyperparameter tuning, model evaluation, and model optimization.
• Ability to research opportunities quantitatively, define technical strategy, set scope, manage timelines, and drive measurable outcomes.
• Strong cross-functional communication and collaboration skills.

Nice-to-haves

• Experience optimizing a two-sided marketplace or similarly complex 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, ML engineering workflows, model monitoring, Airflow, dbt, or similar infrastructure.
• Master’s degree or PhD in a relevant quantitative field.

Compensation and Benefits

• Total compensation targets by US zone:
  • Zone 1: $231,000–$280,000, including $203,000–$238,000 base salary, plus equity.
  • Zone 2: $214,000–$259,000, including $188,000–$220,000 base salary, plus equity.
  • Zone 3: $196,000–$238,000, including $172,000–$202,000 base salary, plus equity.
• Medical, dental, and vision coverage with premiums covered for employees and dependents.
• Unlimited flexible time off, company holidays, recharge days, and a holiday break.
• Home office, health and wellness, parental and family leave, fertility, and family-forming reimbursements or benefits.
• 401(k) plan, team events, and company meetups.