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Data Scientist Project Manager Jobs in Michigan (NOW HIRING)

DTE Energy

Expert Data Scientist

Detroit, MI · On-site

Oversees and coaches the team of data scientists to run analytical experiments methodically ... Advanced project management skills * Experience with publications or conference presentations in a ...

DTE Energy

Expert Data Scientist

Detroit, MI · On-site

Oversees and coaches the team of data scientists to run analytical experiments methodically ... Advanced project management skills * Experience with publications or conference presentations in a ...

Munich Re

Data Scientist

Ann Arbor, MI · On-site +1

Develop and project manage modeling projects and statistical analyses. * Effectively communicate ... Data Scientist: 2 or more years of data science/predictive analytics experience in (re)insurance or ...

Munich RE

Data Scientist

Ann Arbor, MI · On-site

Develop and project manage modeling projects and statistical analyses. * Effectively communicate ... Data Scientist: 2 or more years of data science/predictive analytics experience in (re)insurance or ...

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Data Scientist Project Manager information

What is a data scientist project manager?

A Data Scientist Project Manager is a professional who oversees data science projects from conception through completion, ensuring that project goals align with business objectives. They bridge the gap between data science teams and stakeholders, managing timelines, resources, and communication. In addition to technical knowledge in data science and analytics, they possess strong project management skills to coordinate tasks, mitigate risks, and deliver results. Their role is essential for translating complex data-driven insights into actionable business strategies. They often use methodologies like Agile or Scrum to guide project workflows and adapt to changing requirements.

What are the key skills and qualifications needed to thrive as a data scientist project manager?

To thrive as a Data Scientist Project Manager, you need a solid background in data science, analytics, and project management, often supported by degrees in computer science, statistics, or business and certifications like PMP or Agile. Familiarity with tools such as Python, R, SQL, project management software (e.g., Jira, Trello), and cloud platforms is crucial. Excellent communication, leadership, and problem-solving abilities help bridge gaps between technical teams and stakeholders. These skills ensure successful project delivery by aligning data-driven insights with business objectives and effective team coordination.

How do data scientist project managers typically balance technical data work with project management responsibilities?

Data Scientist Project Managers often split their time between hands-on data analysis and overseeing project progress. They commonly coordinate with cross-functional teams, set project timelines, and ensure that data solutions align with business objectives while occasionally contributing code or analytical insights. Effective communication and time management are essential, as they must bridge the gap between technical teams and stakeholders. This dual responsibility offers exposure to both technical growth and leadership development, making it ideal for professionals seeking advancement into higher management roles.

What is the difference between Data Scientist Project Manager vs Data Analyst Project Manager?

AspectData Scientist Project ManagerData Analyst Project Manager
Required CredentialsBachelor's/Master's in Data Science, Analytics, or related fields; certifications like PMP or AgileBachelor's in Data Analysis, Business, or related fields; certifications like PMP or Agile
Work EnvironmentLeads data science projects, collaborates with data scientists and engineersManages data analysis projects, works with analysts and business teams
Employer & Industry UsageTech companies, finance, healthcare, industries with advanced analyticsRetail, marketing, finance, industries relying on data reporting

The main difference is that Data Scientist Project Managers oversee data science initiatives involving complex modeling and algorithms, while Data Analyst Project Managers focus on managing data reporting and analysis projects. Both roles require project management skills and relevant certifications, but their technical focus and team collaboration differ.

Can a data scientist become a project manager?

A data scientist can transition to a project manager role by developing skills in leadership, communication, and project planning, often supported by certifications like PMP or Agile. Their technical expertise can be valuable in managing data-driven projects, but they typically need to gain experience in managing teams and project workflows.

What are popular job titles related to Data Scientist Project Manager jobs in Michigan?

For Data Scientist Project Manager jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Data Scientist Project Manager jobs in Michigan look for?

The top searched job categories for Data Scientist Project Manager jobs in Michigan are:

What cities in Michigan are hiring for Data Scientist Project Manager jobs?

Cities in Michigan with the most Data Scientist Project Manager job openings:

Data Scientist Project Manager jobs near you
Infographic showing various Data Scientist Project Manager job openings in Michigan as of August 2026, with employment types broken down into 82% Full Time, 13% Part Time, 2% Temporary, 2% Contract, and 1% Nights. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution.

Expert Data Scientist

DTE Energy

Detroit, MI • On-site

Full-time

Re-posted 5 days ago

Key responsibilities

  • Leads data science projects from end-to-end, collaborating with stakeholders and integrating solutions into business processes

  • Conducts advanced statistical analysis to identify trends and recommend improvements

  • Develops complex data sets and predictive models to support decisions related to safety, efficiency, and customer satisfaction

DTE Energy rating

8.6

Company rating: 8.6 out of 10

Based on 60 frontline employees who took The Breakroom Quiz

11th of 53 rated energy and utility

Job description

DTE is one of the nation's largest diversified energy companies. Our electric and gas companies have fueled our customer's homes and Michigan's progress for more than a century. And as Michigan's largest source of renewable energy, we're creating a cleaner, healthier environment to power our future. We're also serving communities beyond Michigan, where our affiliated businesses offer renewable energy, emission control technologies, and energy services to industries in 19 states.
But we're more than a leading energy company... and working at DTE is more than just a job. At DTE, we take great care of each other and our customers, and we use our energy to be a force for growth and prosperity in our communities. When you join us, you'll be part of a team that welcomes, recognizes, and celebrates differences and values everyone's health, safety, and wellbeing. Are you ready to make that kind of difference? Bring your energy to DTE. Together, we can achieve great things.
Testing Required: Not Applicable
Hybrid Role: This role is hybrid, with an established schedule of in-person work required at an assigned work location. Any remote work is expected to be performed from an employee's primary residence, unless allowed (or prohibited) through the Company's remote work guidelines.
Emergency Response: Yes - Must be available to perform a primary assignment in support of DTE's emergency response to storms or other events that impact service to our customers.
Job Summary
Acts as a technical expert and project leader for the most challenging data science projects. Provides highly technical and analytical assessments of business priorities to senior leadership and drives the implementation of analytic solutions. Acts as a strong influencer and change agent in the organization to advance a data-based decision-making culture. Oversees and coaches the team of data scientists to run analytical experiments methodically, evaluate alternative models, and develop predictive models to forecast business performance metrics. Communicates effectively with technical and non-technical stakeholders with strong domain expertise and business acumen. Leads the research and development of new technologies and best practices within the industry to recommend approaches and strategies that develop the organization's analytical capabilities-Span of Control: 0, Individual Contributor.
Key Accountabilities
  • Leads data science projects from end-to-end, collaborating with cross-functional stakeholders, identifying business requirements, gathering data, researching analytics solutions, and integrating solutions into business processes
  • Conducts advanced statistical analysis to determine trends and significant data relationships, and proactively recommends areas of improvement
  • Develops complex data sets and predictive models to support key decisions to improve safety, employee engagement, operation efficiency, product quality, and customer satisfaction (e.g., cost-benefit, invest-divest, forecasting, predictive, what-if, impact analysis, etc.)
  • Prepares and delivers insightful presentations and action recommendations. Educates leaders and crews on complex analytical findings in laymen terms and with storytelling/data visualization
  • Identifies and evaluates technologies and provides strategic inputs to advance the organization's analytics capabilities
  • Mentors the team of data scientists by guiding their professional development in conjunction with the team manager
  • Actively researches new technologies in related subjects and supports the organization to develop data analytics strategies and roadmaps

Minimum Education & Experience Requirements
This is a multi-track base requirement job; education and experience requirements can be satisfied through one of the following options:
  • Bachelor's degree and 10 years of experience, inclusive of 3 years of leading experience working as a team or project lead in a data analytical or computer programming function; or
  • Master's degree and 8 years of experience, inclusive of 3 years of leading experience working as a team or project lead in a data analytical or computer programming function; or
  • Ph.D. degree and 6 years of experience, including 3 years of leading experience working as the team or project lead in a data analytical or computer programming function
  • 5 years of experience in qualitative and quantitative analytics (e.g., data mining, regression analysis, hypothesis testing, A/B testing, predictive modeling, model optimization, time series analysis, cluster analysis, natural language processing/text analytics, and segmentation)

Other Qualifications
Preferred:
  • Master's or Ph.D degree with emphasis on coursework of a quantitative nature (e.g., Statistics, Computer Science, Engineering, Mathematics, Physics, Data Science, Econometrics, etc.)
  • Advanced business acumen and utility/energy industry experience
  • Deep interest and aptitude in data, metrics, analysis, trends, statistics, and program evaluation
  • Advanced project management skills
  • Experience with publications or conference presentations in a related subject

Other Requirements:
  • Intermediate-level competency using advanced Excel and statistical tools (e.g., Minitab, Alteryx, advanced Excel with VBA, R, Python, SAS, SPSS, Stata, MATLAB, etc.) to conduct in-depth analysis to support decision making
  • Proven expertise in articulating business questions and pulling data from relational databases (e.g., ORACLE, SQL SERVER)
  • Intermediate-to advanced-level programming skills in SQL, Python, R, and in visualization tools such as Power BI, Tableau
  • Intermediate- to advanced- level skills in data modeling, data structure, metadata, and the application of complex SQL queries with data from multiple sources, including a Big Data platform (e.g., Azure ADLS and Databricks)
  • Experience in designing, building, and supporting a production pipeline for data transformation and validation
  • Advanced skills of applied research design, machine learning, prediction, and optimization (e.g., multivariate statistical analysis, unsupervised and supervised learning, predictive modeling, Monte Carlo simulation)
  • Exceptional track record of successfully delivering large-scale analytical models and systems that result in substantial positive impact on business operations or customer satisfaction
  • Intermediate-level or higher Continuous Improvement knowledge, skills, and certifications
  • Self-starter and learning capability in advancing skillset in business processes, data science, and communications
  • Advanced interpersonal, analytical, and problem-solving skills, including the ability to communicate technical information and complex data analytics to a non-technical audience
  • Experience with leading large projects end-to-end with customer facing and system integration
  • Experience with mentoring and coaching data scientists
  • Experience with agile process development
  • Experience using version control (e.g., Git)

Additional Information
Incumbents may engage in all or some combination of the activities and accountabilities and utilize a variety of the competencies cited in this description depending upon the organization and role to which they are assigned. This description is intended to describe the general nature and level of work performed by incumbents in this job. It is not intended as an all-inclusive list of accountabilities or responsibilities, nor is it intended to limit the rights of supervisors or management representatives to assign, direct and control the work of employees under their supervision.
PRIVACY NOTICE TO CALIFORNIA JOB APPLICANTS
At DTE Energy, we are committed to providing an inclusive workplace where everyone feels welcome and a sense of belonging. We seek individuals with a heart for service, a passion to help our communities prosper, and ideas to help shape the future of energy. We are proud to be an equal opportunity, employer that considers all qualified applicants without regard to race, color, sex, sexual orientation, gender identity, age, religion, disability, national origin, citizenship, height, weight, genetic information, marital status, pregnancy, protected veteran status or any other status protected by applicable federal and/or state laws.

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