Dynamic Pricing Data Scientist — Real-Time ML

United States Digital Space LLC; San Francisco (CA); Hybrid; USD 128,000 - 160,000; Full time

Benefits offered by this job

Health insurance

Mental health

Family benefits

Child care benefits

Pet benefits

401(k) with company match

Paid time off

Job summary

United States Digital Space LLC in San Francisco is seeking a Data Scientist to help build end-to-end dynamic pricing models. You will work with Product and Engineering on a cross-functional team to prototype pricing solutions that scale and impact riders and drivers.

You should have experience in machine learning, statistics, and econometrics, plus strong Python and SQL skills. The role is in-office with a hybrid schedule (3 days/wk) and offers a competitive base salary, equity potential, and

Qualifications

• MS/PhD in CS, Economics, Statistics, Mathematics or related field.
• Experience in building pricing models or econ/ML models.
• Strong Python and SQL skills with data wrangling and visualization.

Responsibilities

• Partner with scientists and pricing teams to formalize problems.
• Perform data analysis to identify root causes.
• Develop and fit statistical, ML, or optimization models.
• Write production code and ship models to production.
• Design and run simulated and live experiments.
• Analyze data from experiments to inform launches.
• Communicate findings to Product, Engineering, and executives.

Skills

Machine Learning

Statistics

Econometrics

Data analysis

Python

SQL

Communication

Collaboration

Causal Inference

Education

MS/PhD in CS/ Econ/ Stats/ Math

Tools

R

Matlab

Jupyter

Job description

United States Digital Space LLC in San Francisco is seeking a Data Scientist to help build end-to-end dynamic pricing models. You will work with Product and Engineering on a cross-functional team to prototype pricing solutions that scale and impact riders and drivers.

You should have experience in machine learning, statistics, and econometrics, plus strong Python and SQL skills. The role is in-office with a hybrid schedule (3 days/wk) and offers a competitive base salary, equity potential, and