Senior Marketplace Scientist: ML, Optimization & Growth

Uber; New York (NY); On-site; USD 190,000 - 211,000; Full time

Benefits offered by this job

Bonus potential

Equity

401(k) plan

Job summary

Uber is seeking a Data Scientist in New York City to lead end-to-end product development from a data and analytics perspective. You will prototype ML, optimization, causal inference models, generate insights, and design experiments that drive Uber's delivery and growth products.

You will work with cross-functional teams to prioritize initiatives, build logging and metrics, and define success metrics, owning the cycle from data science to production.

Qualifications

• Ph.D., MS, or BS in economics, statistics, ML, OR, or other quantitative field.
• 4+ years in industry as an Applied/Data Scientist (or 2+ years with PhD).
• Proficient in at least one programming language and SQL.
• Ability to prototype algorithms and analyze large datasets.

Responsibilities

• Solve ambiguous business problems using data-driven approaches including ML, Optimization, Causal Inference.
• Develop and implement statistical/econometric methodologies to improve validity and generalizability.
• Develop data-driven insights and work with cross-functional teams to prioritize initiatives.
• Design and analyze experiments, presenting actionable recommendations.
• Orient teams around data-driven product development with logging, metrics, and dashboards.
• Define success metrics in collaboration with partners and own end-to-end product development cycle.

Skills

Python

SQL

Data analysis

Communication

Experiment design

Education

Quantitative degree

Tools

Python

R

Java

Scala/Spark

Job description

Uber is seeking a Data Scientist in New York City to lead end-to-end product development from a data and analytics perspective. You will prototype ML, optimization, causal inference models, generate insights, and design experiments that drive Uber's delivery and growth products.

You will work with cross-functional teams to prioritize initiatives, build logging and metrics, and define success metrics, owning the cycle from data science to production.