Education
Degree requirements are flexible. A technical degree in Computer Science, Data Science, Statistics, Mathematics, Operations
Research, Economics, Engineering, or a related field is helpful, but equivalent practical experience is equally valued.
A Master’s or PhD in a quantitative field is a plus, but not required.
Experience
- Applied ML experience: Five or more years of professional experience in applied machine learning, data science, ML engineering,
applied statistics, or a related field, or equivalent demonstrated impact.
- Large-scale data: Experience building and evaluating models using large-scale behavioral, transactional, product, marketing, or
customer data.
- Experimentation: Experience designing experiments, defining success metrics, measuring incrementality, interpreting results, and
translating findings into practical product or business decisions.
Skills
- Machine learning and modeling: Strong Python skills and hands-on experience with common ML frameworks, supervised learning,
model selection, hyperparameter tuning, evaluation, and performance diagnosis.
- Data processing and feature engineering: Strong SQL skills and experience with BigQuery, Spark, or similar platforms for data
collection, cleaning, preprocessing, exploration, and feature development.
- Analytics and experimentation: Strong statistical reasoning and practical knowledge of A/B testing, holdout design, causal
measurement, incrementality, statistical significance, and business-impact analysis.
- Production engineering and MLOps: Experience with cloud ML platforms, deployment pipelines, batch or real-time inference, CI/CD,
model registries, monitoring, observability, retraining, rollback, and scalable system design.
Personal Attributes
- Strong ownership: Takes responsibility for delivering high-quality solutions and measurable outcomes with limited oversight.
- Business-impact orientation: Connects modeling and engineering decisions to customer experience, product performance, and
business value.
- AI-first builder mindset: Enjoys coding, modeling, automating, and shipping while proactively using AI and agentic tools to improve
productivity and quality.
- Clear, collaborative communication: Communicates assumptions, tradeoffs, risks, and results effectively across ML, engineering,
product, analytics, and business teams.
What’s Next
Our hiring process includes the following steps
- Video Introduction: Submit a brief video introducing yourself, your work, and your most relevant experience.
- Technical interview: Demonstrate your applied machine learning, analytical, and engineering capabilities.
- Hiring manager interview: Meet with the hiring manager to discuss your background and fit for the role.
- Final interview: Meet with our AI leadership, including the Chief AI Officer, for a final assessment.