Lead Data Scientist
New
J
JobgetherConsumer technology
Based in United StatesFull-TimeLead
Salary$175,000–$218,000 USD
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Job Details
- Experience
- 6+ years of experience scoping, building, analyzing, and deploying ML-powered systems, including models shipped successfully to production.
- Required Skills
- PythonData AnalysisMachine LearningSoftware Engineeringscikit-learnGenerative AIPySpark
Requirements
- Have an advanced degree in a discipline requiring sophisticated statistical analysis, or equivalent professional experience.
- Bring 6+ years of experience scoping, building, analyzing, and deploying ML-powered systems, including models shipped successfully to production.
- Have significant programming experience with Python, scikit-learn, PySpark, and causal inference tooling.
- Understand software engineering practices including testing, modularization, version control, and production-quality development.
- Have professional experience applying modern causal inference and causal analysis techniques.
- Have experience working with existing datasets, designing instrumentation to generate new data, and implementing transformations for complex analyses and ML development.
- Have hands-on experience designing, monitoring, and analyzing experiments in consumer technology environments.
- Have experience building ML systems or conducting experimentation within a B2C technology company.
- Have experience using LLMs or generative AI for advanced data processing, analysis, development, or research workflows.
- Be able to critically review AI-generated code, analyses, and models and remain accountable for shipped work.
Responsibilities
- Investigate revenue-generating opportunities and analyze data to size, scope, prioritize, and measure product changes.
- Design, build, deploy, and operate production ML systems, including batch inference, online services, and online learning models.
- Partner with Product, Mobile Engineering, Cloud Engineering, Data Engineering, and MLOps teams to integrate ML into customer-facing features.
- Establish monitoring frameworks for the performance, reliability, and business impact of ML-powered experiences.
- Implement data, code, and model lineage practices for secure, reproducible, compliant, and maintainable ML lifecycles.
- Collaborate with Data Engineering to improve data ecosystems and pipelines for experimentation and machine learning.
- Apply causal inference, experimentation, and advanced analytics to product and monetization decisions.
- Mentor data scientists and define best practices for advanced analytics, experimentation, and production ML development.
- Use Claude Code and other AI tools across data discovery, modeling, coding, testing, and experiment evaluation.
- Translate analytical findings into actionable product and revenue strategies with business leaders.
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