Geospatial Data Scientist — ML & DMAP for Store Growth

SmartRecruiters, Inc.; Ann Arbor Charter Township (MI); On-site; USD 90,000 - 120,000; Full time

Benefits offered by this job

Paid holidays

Medical, dental & vision benefits

401k matching

Stock purchase/bonus program

Wellness programs

Job summary

Domino’s Pizza is seeking a data scientist to transform large geospatial datasets into actionable store expansion insights. You’ll develop ML components for an in-house mapping platform and partner with teams to address opportunities using geospatial data and analytics.

The role emphasizes data quality, scalable pipelines, and collaboration across functions, with the chance to influence store development strategies globally.

Qualifications

• Degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering)
• 1–3 years of professional data science or related analytical experience
• Experience with geospatial data analysis and ML components
• Ability to communicate technical findings to non-technical audiences

Responsibilities

• Extract, clean, and preprocess data from multiple sources for analysis.
• Develop baseline statistical models and ML algorithms for business problems.
• Collaborate with cross-functional teams to deliver actionable insights.
• Document methodologies and results for reproducibility and knowledge sharing.
• Monitor data pipelines and analytical tools to ensure quality and performance

Skills

Geospatial data analysis

Python

SQL

ML concepts

Data storytelling

Communication

Git

Education

Degree in CS/Stats/Math/Engineering

Tools

ArcGIS (arcpy)

Pandas/GeoPandas

Geospatial SQL

Tableau/Power BI

Jupyter Notebooks

Git

Matplotlib/Seaborn

Job description

Domino’s Pizza is seeking a data scientist to transform large geospatial datasets into actionable store expansion insights. You’ll develop ML components for an in-house mapping platform and partner with teams to address opportunities using geospatial data and analytics.

The role emphasizes data quality, scalable pipelines, and collaboration across functions, with the chance to influence store development strategies globally.