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Data Science Jobs in Rochester, MI (NOW HIRING)

Deloitte

Lead AI and Data Science Engineer II

Detroit, MI

$101K - $133K/yr

Lead AI and Data Science Engineer II Drive the design and delivery of advanced analytics, artificial intelligence (AI), and generative artificial intelligence (GenAI) solutions that inform Talent ...

Conclusive Marketing

Data Scientist

Detroit, MI · On-site

Data Scientist Role Summary OneMagnify's Data Scientists sit at the intersection of client strategy ... BA/BS in Computer Science, Statistics, Mathematics, MIS, Marketing Research, or a related ...

Deloitte

Agentic AI, AI & Data Science Engineer

Detroit, MI · On-site

$113K - $136K/yr

Work you'll do As an AI and Data Science Engineer III on the AI & Data team, you will be responsible for driving technology-focused client delivery across complex engagements. Working within an ...

Pwc

AI & GenAI Data Scientist-Director

Detroit, MI · On-site

$155K - $410K/yr

Industry/Sector Not Applicable Specialism Data Science Management Level Director & Summary The Opportunity As an AI & GenAI Data Scientist-Director, you will leverage advanced technologies and ...

Stellantis

ICT Data Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

You will be part of a talented team of data scientists, engineers, driving predictive analytics and early detection of emerging warranty trends using vast datasets across the enterprise. Key ...

Stellantis

ICT Data Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

You will be part of a talented team of data scientists, engineers, driving predictive analytics and early detection of emerging warranty trends using vast datasets across the enterprise. Key ...

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Showing results 21-40

All JobsData Science JobsData Science Jobs in Rochester, MI

Data Science information

See Rochester, MI salary details

$34.5K

$113K

$180.9K

How much do data science jobs pay per year?

As of Aug 7, 2026, the average yearly pay for data science in Rochester, MI is $112,975.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,700.00 and $125,200.00 per year, depending on experience, location, and employer.

Is a data scientist in high demand?

Yes, data scientists are in high demand across many industries due to the increasing reliance on data-driven decision making. The role requires skills in programming, statistics, and machine learning, and job growth is expected to continue as organizations expand their data capabilities.

What are the key skills and qualifications needed to thrive as a data scientist, and why are they important?

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

What does a data scientist do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.
More about Data Science jobs
What are the most commonly searched types of Data Science jobs in Rochester, MI? The most popular types of Data Science jobs in Rochester, MI are:
What are popular job titles related to Data Science jobs in Rochester, MI? For Data Science jobs in Rochester, MI, the most frequently searched job titles are:
What job categories do people searching Data Science jobs in Rochester, MI look for? The top searched job categories for Data Science jobs in Rochester, MI are:
What cities near Rochester, MI are hiring for Data Science jobs? Cities near Rochester, MI with the most Data Science job openings:
Data Science jobs near you
Infographic showing various Data Science job openings in Rochester, MI as of August 2026, with employment types broken down into 100% Full Time. Highlights an 80% In-person, 4% Hybrid, and 16% Remote job distribution, with an average salary of $112,975 per year, or $54.3 per hour.

Lead AI and Data Science Engineer II

Deloitte

Detroit, MI

$101K - $133K/yr

Other

Posted 8 days ago

Deloitte rating

8.2

Company rating: 8.2 out of 10

Based on 92 frontline employees who took The Breakroom Quiz

45th of 150 rated financial services

Job description

Lead AI and Data Science Engineer II

Drive the design and delivery of advanced analytics, artificial intelligence (AI), and generative artificial intelligence (GenAI) solutions that inform Talent strategy and workforce decisions. In this role, you will lead complex data science work that combines research design, statistical analysis, machine learning, and application development to solve high-priority people challenges. You will also partner with business, Talent, and technology stakeholders to translate workforce data into actionable insights, tools, and recommendations.

Recruiting for this role ends on August 10, 2026.

Work You'll Do

In this role, you will lead the data science dimension of that work, shaping research design, analytical methodology, and solution development while helping develop junior data scientists across the full analytics lifecycle.

Strategy & Stakeholder Partnership

  • Partner with the Advanced Analytics & AI leader to help shape and execute the People Analytics portfolio, using data-driven insights to inform Talent strategy, establish priorities, and manage multiple initiatives.
  • Collaborate with stakeholders across Talent, business leadership, and ITS to define business needs, identify analytics opportunities, communicate complex technical concepts, and advise on the benefits and limitations of automation and artificial intelligence (AI).
  • Develop and drive strategy to understand and improve data quality across relevant Talent data assets.

Analytics & Data Science

  • Design and structure analytical work, including research design, project planning, and use case development, and apply advanced statistical and machine learning methods to generate rigorous, actionable insights.
  • Perform analytics in cloud-based environments, support the development of clear leadership-ready presentations, and stay current on developments in data science, behavioral science, and adjacent disciplines.

Software & Data Engineering

  • Develop full-stack, web-based data and generative artificial intelligence (GenAI) applications that improve Talent reporting and business operations.
  • Partner with data engineering teams on pipeline architecture and infrastructure, using knowledge of extract, transform, load (ETL), version control, and continuous integration and continuous delivery (CI/CD) concepts to inform technical decisions.

People Leadership

  • Mentor junior and mid-level data scientists across the analytics lifecycle, from problem framing and data collection through analysis and synthesis of findings.
  • Provide structure and oversight for analytically complex projects, while assessing talent, delivering developmental feedback, and contributing to a high-performing analytics team.

A successful candidate would possess these skills:

  • Ability to work independently and collaborate as part of a team
  • Effective written and verbal communication skills
  • Meticulous attention to detail and quality of work product
  • Ability to build and sustain professional relationships
  • Ability to lead projects or workstreams
  • Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
  • Strong interpersonal skills and professional demeanor
  • Ability to meet deadlines
  • Ability to mentor and provide clear guidance to others

The team

The Talent Experience & Engagement, People Analytics - Advanced Analytics & AI team helps the firm make informed people and business decisions by translating workforce data into actionable insights, tools, and strategies. The team combines behavioral science, organizational research, advanced analytics, and artificial intelligence (AI) to address leadership questions, improve decision-making, and create scalable solutions with measurable impact.

The work is delivered through two connected capabilities. Organizational research and storytelling ground analyses in rigorous social science and clear, decision-oriented narratives. Data science applies statistical analysis, machine learning, and AI to identify meaningful patterns in workforce data, build data and generative artificial intelligence (GenAI) applications, and partner with Talent data engineering teams on pipelines and infrastructure.

Qualifications

Required:

  • Graduate degree in Applied Statistics, Computer Science, Life Sciences, Industrial-Organizational Psychology, Organizational Behavior, Sociology, Economics, Anthropology, or another quantitative discipline
  • 6+ years of experience in data science, analytics, or applied research
  • Experience using Python and Structured Query Language (SQL), with working knowledge of JavaScript, TypeScript, HyperText Markup Language (HTML), and Cascading Style Sheets (CSS)
  • Experience applying multivariate statistics and machine learning methods, including regression, structural equation modeling, factor analysis, decision trees, clustering, and dimension reduction, in Apache Spark or Databricks environments
  • Experience developing artificial intelligence (AI) or generative artificial intelligence (GenAI) applications and working with extract, transform, load (ETL), pipeline design, and orchestration concepts
  • Demonstrated experience leading AI strategy and enabling adoption of AI or data engineering tools within a complex organizational environment
  • Mentorship of junior and mid-level analysts, providing guidance across the analytics lifecycle from conception through data mining/analysis and synthesis of results
  • Ability to travel 0-10%, on average, based on the work you do and the clients and industries/sectors you serve.
  • Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.

Preferred:

  • Doctor of Philosophy (PhD) in Applied Statistics, Computer Science, Life Sciences, Industrial-Organizational Psychology, Organizational Behavior, Sociology, Economics, Anthropology, or another quantitative discipline
  • Experience in a professional services organization or large matrixed organization
  • Experience designing and building end-to-end data pipelines for reporting and analytics
  • Experience applying behavioral science frameworks to interpret workforce data and generate insight - not solely technical or statistical proficiency
  • Experience with talent systems such as OneModel or SAP

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $118700 to $218600.

Qualifications:

Lead AI and Data Science Engineer II

Drive the design and delivery of advanced analytics, artificial intelligence (AI), and generative artificial intelligence (GenAI) solutions that inform Talent strategy and workforce decisions. In this role, you will lead complex data science work that combines research design, statistical analysis, machine learning, and application development to solve high-priority people challenges. You will also partner with business, Talent, and technology stakeholders to translate workforce data into actionable insights, tools, and recommendations.

Recruiting for this role ends on August 10, 2026.

Work You'll Do

In this role, you will lead the data science dimension of that work, shaping research design, analytical methodology, and solution development while helping develop junior data scientists across the full analytics lifecycle.

Strategy & Stakeholder Partnership

  • Partner with the Advanced Analytics & AI leader to help shape and execute the People Analytics portfolio, using data-driven insights to inform Talent strategy, establish priorities, and manage multiple initiatives.
  • Collaborate with stakeholders across Talent, business leadership, and ITS to define business needs, identify analytics opportunities, communicate complex technical concepts, and advise on the benefits and limitations of automation and artificial intelligence (AI).
  • Develop and drive strategy to understand and improve data quality across relevant Talent data assets.

Analytics & Data Science

  • Design and structure analytical work, including research design, project planning, and use case development, and apply advanced statistical and machine learning methods to generate rigorous, actionable insights.
  • Perform analytics in cloud-based environments, support the development of clear leadership-ready presentations, and stay current on developments in data science, behavioral science, and adjacent disciplines.

Software & Data Engineering

  • Develop full-stack, web-based data and generative artificial intelligence (GenAI) applications that improve Talent reporting and business operations.
  • Partner with data engineering teams on pipeline architecture and infrastructure, using knowledge of extract, transform, load (ETL), version control, and continuous integration and continuous delivery (CI/CD) concepts to inform technical decisions.

People Leadership

  • Mentor junior and mid-level data scientists across the analytics lifecycle, from problem framing and data collection through analysis and synthesis of findings.
  • Provide structure and oversight for analytically complex projects, while assessing talent, delivering developmental feedback, and contributing to a high-performing analytics team.

A successful candidate would possess these skills:

  • Ability to work independently and collaborate as part of a team
  • Effective written and verbal communication skills
  • Meticulous attention to detail and quality of work product
  • Ability to build and sustain professional relationships
  • Ability to lead projects or workstreams
  • Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
  • Strong interpersonal skills and professional demeanor
  • Ability to meet deadlines
  • Ability to mentor and provide clear guidance to others

The team

The Talent Experience & Engagement, People Analytics - Advanced Analytics & AI team helps the firm make informed people and business decisions by translating workforce data into actionable insights, tools, and strategies. The team combines behavioral science, organizational research, advanced analytics, and artificial intelligence (AI) to address leadership questions, improve decision-making, and create scalable solutions with measurable impact.

The work is delivered through two connected capabilities. Organizational research and storytelling ground analyses in rigorous social science and clear, decision-oriented narratives. Data science applies statistical analysis, machine learning, and AI to identify meaningful patterns in workforce data, build data and generative artificial intelligence (GenAI) applications, and partner with Talent data engineering teams on pipelines and infrastructure.

Qualifications

Required:

  • Graduate degree in Applied Statistics, Computer Science, Life Sciences, Industrial-Organizational Psychology, Organizational Behavior, Sociology, Economics, Anthropology, or another quantitative discipline
  • 6+ years of experience in data science, analytics, or applied research
  • Experience using Python and Structured Query Language (SQL), with working knowledge of JavaScript, TypeScript, HyperText Markup Language (HTML), and Cascading Style Sheets (CSS)
  • Experience applying multivariate statistics and machine learning methods, including regression, structural equation modeling, factor analysis, decision trees, clustering, and dimension reduction, in Apache Spark or Databricks environments
  • Experience developing artificial intelligence (AI) or generative artificial intelligence (GenAI) applications and working with extract, transform, load (ETL), pipeline design, and orchestration concepts
  • Demonstrated experience leading AI strategy and enabling adoption of AI or data engineering tools within a complex organizational environment
  • Mentorship of junior and mid-level analysts, providing guidance across the analytics lifecycle from conception through data mining/analysis and synthesis of results
  • Ability to travel 0-10%, on average, based on the work you do and the clients and industries/sectors you serve.
  • Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.

Preferred:

  • Doctor of Philosophy (PhD) in Applied Statistics, Computer Science, Life Sciences, Industrial-Organizational Psychology, Organizational Behavior, Sociology, Economics, Anthropology, or another quantitative discipline
  • Experience in a professional services organization or large matrixed organization
  • Experience designing and building end-to-end data pipelines for reporting and analytics
  • Experience applying behavioral science frameworks to interpret workforce data and generate insight - not solely technical or statistical proficiency
  • Experience with talent systems such as OneModel or SAP

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to ski...

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