Data Scientist

Job Description

Job Description

Company Description

Domino’s Pizza, which began in 1960 as a single store location in Ypsilanti, MI, has had a lot to celebrate lately: we’re a reshaped, reenergized brand of honesty, transparency and accountability – not to mention, great food! In the rise to becoming a true technology leader, the brand is now consistently one of the top five companies in online transactions and 65% of our sales in the U.S. are taken through digital channels. The brand continues to deliver the dream’ to local business owners, 90% of which started as delivery drivers and pizza makers in our stores. That’s just the tip of the iceberg…or as we might say, one “slice” of the pie! If this sounds like a brand you’d like to be a part of, consider joining our team!

Job Description

 

Drive new store openings in our domestic and largest international markets by transforming large geospatial datasets into actionable insights. Build a strong knowledge of available internal and external data sources. Mine and synthesize across data sources to help address opportunities leveraging geospatial data and analytic techniques. Conduct analyses to address ad hoc requests of ongoing project work. Assist in developing the machine learning (ML) and artificial intelligence (AI) components of an in-house Store Development Mapping Platform (DMAP), creating test and measurement plans and executing those plans. Continually innovate and improve upon existing methodologies and products. Consult on data collection for new products to ensure all tracking is in place for future analysis. Manipulate and analyze large, complex geospatial datasets, ensuring data quality and accessibility for advanced analytics. Work in a fast-paced, challenging environment with opportunities for skill enhancement.

 

Responsibilities

• Conducts data collection, cleaning, and preprocessing to prepare datasets for analysis.
• Develops and implements basic statistical models and machine learning algorithms to address defined business problems.
• Collaborates with cross-functional teams to understand data requirements and deliver actionable insights.
• Documents methodologies, processes, and results to ensure reproducibility and knowledge sharing.
• Monitors model performance and assists in maintaining data pipelines and analytical tools.

 

Job Tasks

• Extracts and transforms data from multiple sources using standard querying and scripting techniques.
• Builds and validates predictive models using established algorithms and frameworks.
• Generates reports and visualizations to communicate findings to stakeholders.
• Assists in troubleshooting data quality issues and refining data collection processes.
• Participates in code reviews and applies feedback to improve analytical solutions.

 

Skills

• Proficient in data manipulation and exploratory data analysis with attention to data quality.
• Basic understanding of machine learning concepts and ability to apply standard algorithms.
• Effective communication skills for presenting technical information to non-technical audiences.
• Ability to work under general supervision while managing multiple routine tasks.
• Familiarity with version control and collaborative development practices.
• Experience with geospatial data analysis tools, including ArcGIS (arcpy), Pandas/GeoPandas, Geospatial SQL, etc

 

Software Application

• Python for data analysis and modeling.
• SQL for data extraction and manipulation
• Experience with ArcGIS.
• Data visualization tools such as Tableau, Power BI, or matplotlib/seaborn libraries.
• Jupyter Notebooks or similar integrated development environments.

• Version control systems such as Git.
• Experience with Fast API creation (i.e. functional programming, automated testing, performance optimization techniques)
• Experience applying algorithms to large-scale data (e.g. supervised/unsupervised, optimization/solver)
• Experience with Geospatial data types

 

 

Physical Requirements

Primarily sedentary work involving extended periods of computer use. Occasional requirement to attend meetings or collaborate in team environments.

Qualifications

Years Of Experience

1 to 3 years of professional experience in data science or related analytical roles.

 

Education

Completion of a degree in a quantitative field such as Computer Science, Statistics, Mathematics, Engineering, or a related discipline.

Additional Information

Benefits:
• Paid Holidays and Vacation   
• Medical, Dental & Vision benefits that start on the first day of employment
• No-cost mental health support for employee and dependents
• Childcare tuition discounts
• No-cost fitness, nutrition, and wellness programs 
• Fertility benefits
• Adoption assistance
• 401k matching contributions   
• 15% off the purchase price of stock   
• Company bonus   

All your information will be kept confidential according to EEO guidelines.