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Graduate Data Science Intern Jobs in Michigan (NOW HIRING)

Data Scientist

Ann Arbor, MI ยท On-site

$74K/yr

Mathematics, statistics, computer science, data science or field directly related to the position ... Only graduate-level education in excess of the amount required for the next lower level may be ...

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Graduate Data Science Intern information

What does a Graduate Data Science Intern do?

A Graduate Data Science Intern assists data science teams by analyzing large datasets, developing predictive models, and supporting data-driven decision-making. They often work with programming languages like Python or R, and use tools such as SQL, Excel, and machine learning libraries. Interns contribute to real-world projects, gain hands-on experience in data analysis, and help communicate findings to stakeholders. This role serves as a bridge between academic learning and practical application in the workplace.

What types of projects do Graduate Data Science Interns typically work on, and how do these projects support their professional development?

Graduate Data Science Interns often work on real-world data analysis projects such as building predictive models, cleaning and visualizing data, or assisting with machine learning algorithm implementation. These projects are usually part of larger team initiatives and provide interns with hands-on experience using industry-standard tools and methodologies. Collaborating closely with data scientists, engineers, and business stakeholders, interns gain exposure to the end-to-end data science workflow and receive mentorship to support their growth. This experience not only enhances their technical skills but also helps them develop problem-solving and communication abilities crucial for future career advancement.

What are the key skills and qualifications needed to thrive as a Graduate Data Science Intern, and why are they important?

To thrive as a Graduate Data Science Intern, you need foundational knowledge in statistics, machine learning, and data analysis, typically supported by a degree in a quantitative field. Familiarity with programming languages like Python or R, experience with data visualization tools, and understanding of databases are often required. Strong problem-solving abilities, curiosity, and effective communication help interns collaborate and present insights clearly. These skills enable interns to extract actionable information from data and contribute value to team projects.

What is the difference between Graduate Data Science Intern vs Data Analyst Intern?

AspectGraduate Data Science InternData Analyst Intern
Required CredentialsTypically pursuing or recently completed a degree in Data Science, Statistics, or related fieldOften pursuing or recently completed a degree in Data Analysis, Business, or related field
Work EnvironmentResearch-focused, data modeling, machine learning projects, collaborative teamsData collection, cleaning, reporting, visualization tasks
Employer & Industry UsageTech companies, finance, healthcare, academiaRetail, marketing, consulting, finance

The Graduate Data Science Intern role typically involves working on machine learning models and advanced analytics, requiring a background in data science or related fields. In contrast, Data Analyst Interns focus more on data cleaning, visualization, and reporting. Both roles are entry-level, often in similar industries, but differ in technical depth and project scope.

What cities in Michigan are hiring for Graduate Data Science Intern jobs? Cities in Michigan with the most Graduate Data Science Intern job openings:
Data Scientist Machine Learning Practitioner

Data Scientist Machine Learning Practitioner

BlueConduit

Ann Arbor, MI โ€ข On-site

$140K - $150K/yr

Full-time

Medical, Dental, Vision, Retirement

Posted 26 days ago


Job description

Company overview

BlueConduit is an infrastructure analytics SaaS company and social enterprise helping communities make better, faster, and more equitable decisions about critical water infrastructure. Our founding team pioneered predictive modeling for lead service line replacement in Flint, Michigan, and BlueConduit now works with hundreds of cities and utilities across North America.

Our platform helps utilities, municipalities, government agencies, and consultants combine fragmented infrastructure records, field observations, geospatial data, and predictive models to identify risk, prioritize work, meet compliance requirements, and communicate clearly with the public. We are a remote-first team committed to using data science for social good and building tools that are trusted by the people making high-stakes infrastructure decisions.

The role

BlueConduit is hiring a Data Scientist to improve and expand the machine learning models at the core of our infrastructure analytics platform. In this role, you will work on models that help cities prioritize infrastructure investments, reduce risk, and improve drinking water outcomes. You will strengthen our existing modeling workflows, help launch new model products and asset classes, and communicate results clearly to both technical and nontechnical audiences.

This is a strong fit for someone who combines rigorous applied ML judgment with product-minded execution: you enjoy messy real-world data, care about model validation and uncertainty, can build repeatable workflows rather than one-off analyses, and like explaining technical work to people who need to act on it.

In this role you will be expected to be using the latest available AI tools to code productively. You will need to understand what you're building and coding, and understand agentic AI workflows that involve best practices, including unit tests, built-in code reviews, and extensive documentation in your commits for fellow data scientists and software engineers.

This role reports to the VP of Data Science.

What you'll do
  • Build, validate, and improve machine learning and statistical models used in BlueConduit's infrastructure analytics products
  • Help design, build, and launch new model products and model classes that broaden the assets and risks BlueConduit can predict
  • Improve data science workflows, model evaluation, reproducibility, and handoffs into software/product systems
  • Work with heterogeneous municipal, infrastructure, geospatial, and field-observation datasets to generate actionable risk predictions
  • Design validation approaches and communicate model uncertainty, limitations, and tradeoffs clearly to internal teams and customers
  • Use modern AI coding tools such as Claude Code, Codex, or similar systems to accelerate development while applying strong independent programming judgment
  • Use multiple AI agents to contribute to extremely robust workflows and code pipelines with built-in testing and reviews
  • Support customer-facing analysis and present findings in ways that are clear, accurate, and useful for nontechnical decision-makers
  • Contribute to R&D that scales the impact, reliability, and reach of BlueConduit's predictive methods

BlueConduit is a small, remote, and growing team, so this is an opportunity to shape both the role and the next generation of our data science products.

What we're looking for
  • Strong Python-based data science experience, including pandas, NumPy, scikit-learn, and production-quality analysis workflows
  • An undergraduate degree in a quantitative field (e.g., CS, math, stats, physics)
  • Experience building, validating, and improving machine learning or statistical models on messy real-world data
  • Experience building repeatable data science workflows in a product at a SaaS company or similarly operational environment
  • Ability to communicate modeling results, uncertainty, and tradeoffs clearly to technical and nontechnical stakeholders
  • Fluency using modern AI coding tools including coordinating work of AI agents to accelerate development, grounded in strong independent programming ability and judgment
  • Strong data visualization, verbal communication, and written communication skills
  • Comfort with Git-based development workflows
  • Attention to detail, curiosity, and commitment to building models that are understandable, usable, and trusted by the people making infrastructure decisions
  • Passion for socially impactful data science, environmental justice, and public-interest technology
We're especially interested in candidates with one or more of the following
  • A rigorous graduate degree in a quantitative field, or equivalent applied experience
  • Experience modeling asset classes beyond BlueConduit's current water distribution portfolio, such as fire risk, wastewater, hydraulic systems, climate risk, insurance risk, or other infrastructure domains
  • Experience with geospatial data, GIS systems, GeoPandas, or spatial modeling
  • Experience creating a new model product or extending an existing model product to a new domain or asset class
  • Experience with both global/cross-location models and local/site-specific models
  • Experience with methodologies beyond classical ML, such as neural networks, transformers, transfer learning, or other modern ML approaches
  • Experience with cloud-based model workflows, model tracking, versioning, Databricks, PySpark, or distributed computing
  • Familiarity with infrastructure, water quality, government data, or regulated public-sector decision environments
  • Experience working in Agile product development environments
  • Aptitude and interest in building with rapid iteration cycles involving prototyping, receiving feedback, and rebuilding
Location

Remote

Compensation
  • Expected salary range: $140,000$150,000, commensurate with experience
  • Equity options
  • Health, vision and dental benefits
  • Simple IRA benefit with company contribution matching

Every qualified applicant will receive consideration for employment without regard to race, age, color, religion, sex, sexual orientation, or national origin.