Geospatial Data Scientist: ML for Last-Mile Logistics

Shipt; California (MO); Hybrid; USD 76,000 - 130,000; Full time

Benefits offered by this job

Medical, dental, vision

401(k) plan

Discretionary vacation

Paid holidays

Annual bonus eligibility

Restricted stock units (RSUs)

Job summary

Shipt is seeking a Data Scientist specializing in geospatial intelligence to power next‑gen final‑mile logistics and build advanced models and data systems that leverage location, routing, traffic, and spatial signals to enhance delivery success and reduce costs.

You will work on production ML models, handle large geospatial datasets, and collaborate with teams to improve experiences for customers and drivers in a hybrid work environment.

Qualifications

• MS or PhD in Statistics, Applied Mathematics, Computer Science, or other quantitative fields
• Experience building production ML models deployed in real-world systems
• Experience with geospatial data (ex. GPS traces, spatial indexing systems, or routing systems)
• Strong understanding of predictive modeling, feature engineering, experimentation and causal inference
• Strong Python skills with libraries such as: pandas, scikit-learn, PyTorch or TensorFlow
• Ability to work with large datasets and distributed systems (ex. Spark, BigQuery, Snowflake, etc.)

Responsibilities

• Develop advanced geospatial ML models and data systems
• Leverage location, routing, traffic and spatial signals to improve delivery success and reduce costs
• Collaborate with cross-functional teams to enhance customer and driver experience

Skills

Geospatial data

Predictive modeling

Experimentation

Causal inference

Education

MS or PhD in Statistics/Applied Mathematics/CS

Tools

Python

Pandas

Scikit-learn

PyTorch/TensorFlow

Job description

Shipt is seeking a Data Scientist specializing in geospatial intelligence to power next‑gen final‑mile logistics and build advanced models and data systems that leverage location, routing, traffic, and spatial signals to enhance delivery success and reduce costs.

You will work on production ML models, handle large geospatial datasets, and collaborate with teams to improve experiences for customers and drivers in a hybrid work environment.