Staff Data Scientist
E
EvolveVacation Rental
Remote - US. We can hire from anywhere in the U.S. except D.C. and Hawaii.Full-TimeStaff
Salary$180,000–$195,000
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonSQLMachine LearningData modelingMLOps
Requirements
- 6+ years applying data science, statistics, econometrics, or machine learning to consequential business problems.
- Evidence of Staff-level scope: led ambiguous, cross-functional DS or ML work from idea through sustained production use.
- Strong Python and SQL skills, including the ability to write tested, maintainable production code and work effectively with large datasets.
- Experience owning production ML systems, including deployment or scoring, monitoring, failure handling, operational support, and iteration after launch.
- Strong applied-statistics and model-evaluation judgment, including experimentation or causal-inference fundamentals.
- Ability to contribute to economic or optimization work.
- Ability to connect technical decisions to business outcomes and communicate tradeoffs clearly to technical, product, business, and executive audiences.
- Self-directed, collaborative working style with ability to influence without formal authority.
Responsibilities
- Lead the technical direction and hands-on delivery of one or two prioritized applied ML or data science initiatives at a time.
- Translate business opportunities into clear decision frameworks, technical approaches, success measures, and plans for adoption and impact evaluation.
- Build and operate ML systems end to end, including data and feature pipelines, training and evaluation, batch or online inference, deployment, monitoring, failure handling, and iteration.
- Contribute directly to forecasting, demand and price-elasticity estimation, causal measurement, and optimization work that supports pricing and revenue decisions.
- Design and analyze experiments and quasi-experiments to evaluate product, model, and policy changes.
- Establish reusable standards for testing, reproducibility, versioning, observability, documentation, and responsible model operation; review designs and code across the team.
- Partner with Product, Engineering, Data Engineering, Revenue Management, and other business owners to make technical tradeoffs and integrate models into products and operating workflows.
- Mentor data scientists, raise the team's technical judgment, and create leverage beyond your own projects while remaining hands-on.
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