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Executive Data Science Jobs in Michigan (NOW HIRING)

Stellantis

Data Scientist

Auburn Hills, MI · On-site

Your mission is to build and scale trusted data science products that power marketing performance ... Develop defensible, well-documented methodologies that stand up to executive scrutiny and support ...

Denso

Data Scientist 2

Southfield, MI · On-site

$90K - $113K/yr

... executive-ready presentations and reports, explaining statistical concepts and analytical outcomes to non-technical and senior audiences. * Contribute to and help establish analytics and data science ...

Denso

Data Scientist 2

Southfield, MI

$90K - $113K/yr

... executive-ready presentations and reports, explaining statistical concepts and analytical outcomes to non-technical and senior audiences. * Contribute to and help establish analytics and data science ...

Venteon

Senior Director Data Analytics & AI

Novi, MI · On-site

This executive-level position will drive the organization's vision across Master Data Management ... Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related ...

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Executive Data Science information

What are the key skills and qualifications needed to thrive as an Executive Data Scientist, and why are they important?

To thrive as an Executive Data Scientist, you need deep expertise in statistics, machine learning, and data analysis, typically supported by an advanced degree in a quantitative field. Proficiency with data platforms (such as SQL, Hadoop, or Spark), programming languages (like Python or R), and familiarity with data visualization tools is essential, along with certifications like Certified Analytics Professional (CAP) being advantageous. Strategic vision, leadership, and the ability to communicate complex insights to non-technical stakeholders are vital soft skills. These competencies drive effective data-driven decision-making and ensure alignment between analytics initiatives and business objectives.

What is Executive Data Science?

Executive Data Science refers to the leadership and management of data science initiatives within an organization. Professionals in this role are responsible for setting the strategic direction for data-driven projects, overseeing data teams, and ensuring that data science efforts align with business goals. They bridge the gap between technical teams and executives, translating analytical insights into actionable business strategies. Typically, Executive Data Scientists have a blend of technical expertise and strong business acumen, enabling them to make high-level decisions that impact the organization’s growth and innovation.

What is the difference between Executive Data Science vs Data Scientist?

AspectExecutive Data ScienceData Scientist
CredentialsAdvanced degrees (Master's/PhD), leadership experienceBachelor's or Master's in Data Science, Computer Science, or related fields
Work EnvironmentStrategic, leadership-focused, often in executive officesHands-on data analysis, modeling, coding in technical teams
Employer & Industry UsageSenior roles in tech, finance, consulting, and large organizationsTech companies, startups, research institutions, various industries

Executive Data Science roles focus on strategic decision-making, leadership, and overseeing data initiatives, while Data Scientists are primarily involved in technical data analysis and modeling. Both roles require strong analytical skills, but Executive Data Scientists combine technical expertise with leadership responsibilities.

How does an Executive Data Scientist typically collaborate with other departments to drive data-driven decision making?

Executive Data Scientists frequently work cross-functionally with departments such as marketing, product, finance, and operations to identify key business challenges and opportunities where data can provide strategic insights. They lead or advise interdisciplinary teams, translate complex analytics into actionable recommendations, and often present findings to senior leadership or stakeholders. Building strong relationships and understanding business objectives are crucial, as these collaborations enable the alignment of data science initiatives with organizational goals.
What are the most commonly searched types of Data Science jobs in Michigan? The most popular types of Data Science jobs in Michigan are:
What are popular job titles related to Executive Data Science jobs in Michigan? For Executive Data Science jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Executive Data Science jobs in Michigan look for? The top searched job categories for Executive Data Science jobs in Michigan are:
What cities in Michigan are hiring for Executive Data Science jobs? Cities in Michigan with the most Executive Data Science job openings:
Executive Data Science jobs near you
Data Scientist

Data Scientist

Stellantis

Auburn Hills, MI • On-site

Full-time

Posted 23 days ago

Stellantis rating

7.5

Company rating: 7.5 out of 10

Based on 128 frontline employees who took The Breakroom Quiz

15th of 44 rated automakers

Job description

The Commercial Analytics team is looking for a Data Scientist to join our team. Your mission is to build and scale trusted data science products that power marketing performance measurement while promoting data science best practices, actionable recommendations and a high bar for model quality and reliability.
Data scientists work closely with data engineers, analysts, and business teams to design analytics solutions, implement advanced algorithms and evaluate the performance of use cases. Ideal candidates are self-motivated, inquisitive and creative, with a strong desire to solve real-world problems using data.
In this role, you will:
  • Collaborate with business stakeholders to identify high-impact opportunities for statistical and machine learning use cases
  • Design and implement econometric and causal inference models to quantify the impact of vehicle incentives, pricing, and commercial levers on sales, margin, and demand
  • Estimate and interpret price and incentive elasticities across brands, segments, and regions, informing pricing and go-to-market strategies
  • Develop defensible, well-documented methodologies that stand up to executive scrutiny and support strategic decision-making
  • Communicate complex results clearly to both technical and non-technical audiences
  • Partner with Data Engineers to define and source relevant data features for modeling as well as drive adoption and a deep understanding of proper data usage
  • Develop and validate predictive models using techniques such as regression, random forests, gradient boosting, causal modeling and neural networks
  • Communicate findings and recommendations to non-technical audiences through clear visualizations and storytelling
  • Contribute to the maintenance of models in production environments, ensuring scalability and performance
  • Conduct peer code reviews and support best practices in model development and deployment
  • Collaborate with both external and internal resources to support business requirements and key KPI measurement

Basic Qualifications:
  • Bachelor's degree in a quantitative discipline (e.g., Statistics, Economics or other quantitative field)
  • Minimum of 5 years of experience in data science, econometrics or a related field
  • Proficiency in Python and SQL
  • Hands-on experience with big data and cloud platforms such as Databricks, Snowflake or Spark
  • Exposure to MLOps best practices, including model versioning, monitoring, and deployment pipelines
  • Strong grasp of machine learning algorithms like:
    • Regression (linear, logistic)
    • Causal Inference Models (Difference-in Difference, Regression Discontinuity Design)
  • Experience with experimental design, and statistical inference
  • Ability to translate complex data into actionable insights for business stakeholders

Preferred Qualifications:
  • Master's degree in a quantitative discipline (e.g., Statistics, Economics or other quantitative field)
  • Automotive experience
  • Tree-based models (Random Forest, XGBoost, LightGBM)
  • Clustering and dimensionality reduction (e.g., LDA, PCA, Dynamic Time Warping)
  • Experience using PySpark for distributed data processing and feature engineering
  • Experience with Power BI or similar tools for data visualization and dashboarding
  • 2+ years of experience working with finance / pricing / incentives data
  • 2+ years of experience working with sales / commercial data
  • Strong communication and storytelling skills with the ability to influence decision-makers
  • Understanding of CI/CD workflows for automating model testing and deployment
  • Experience working with real-time data pipelines and event-driven architectures

What Stellantis employees say

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