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Internship Data Scientist Risk Jobs in Michigan (NOW HIRING)

Ford Motor Company

Senior Data Scientist

Dearborn, MI · On-site

As a Senior Data Scientist, DCC, you will use your knowledge of data and advanced analytics to ... Balance "doing it right" with "speed to delivery" by identifying and mitigating risk, generating ...

Ford Motor Company

Analytics Scientist

Dearborn, MI · On-site +1

$130K - $169K/yr

... risk management. 4. Utilizing Statistical and Machine Learning Model Development to support ... Extract data from various data sources and platforms (big data platform, GCP, PC, Mainframe, Unix ...

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Internship Data Scientist Risk information

What does an Internship Data Scientist Risk do?

An Internship Data Scientist Risk assists in analyzing data to identify, assess, and mitigate risks within an organization, often in fields like finance, insurance, or technology. They work with senior data scientists to build predictive models, analyze trends, and generate insights that support risk management strategies. Typical tasks include data cleaning, statistical analysis, and reporting findings to help the company make informed decisions. This internship provides hands-on experience with real-world data and risk scenarios, preparing interns for more advanced data science or risk analysis roles.

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

To thrive as an Internship Data Scientist in Risk, you need a solid understanding of statistics, data analysis, and programming languages like Python or R, often supported by coursework in mathematics, data science, or a related field. Familiarity with machine learning libraries (e.g., scikit-learn), data visualization tools, and database management systems is typically required. Strong problem-solving abilities, attention to detail, and effective communication skills help distinguish candidates in this role. These skills are vital for accurately analyzing risk data, developing predictive models, and clearly presenting findings to support risk management decisions.

What types of projects can an internship data scientist in risk expect to work on, and how do these contribute to the organization's goals?

Internship data scientists in risk typically work on projects such as developing predictive models to assess credit risk, analyzing transaction data to detect fraud, and generating insights to improve risk management strategies. These assignments often involve collaborating with experienced data scientists, risk analysts, and business teams to ensure that models are both accurate and actionable. By contributing to these projects, interns help the organization make informed decisions that minimize financial losses and ensure regulatory compliance. The hands-on experience not only builds technical skills but also provides valuable exposure to real-world challenges in the risk domain.
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Internship Data Scientist Risk jobs near you
Data Scientist - Supply Chain

Data Scientist - Supply Chain

Stellantis

Auburn Hills, MI • On-site

Full-time

Posted 3 days ago

Stellantis rating

7.4

Company rating: 7.4 out of 10

Based on 124 frontline employees who took The Breakroom Quiz

17th of 44 rated automakers

Job description

We're building an AI-enabled supply chain that predicts, prescribes, and acts. As a Supply Chain Data Scientist, you'll develop predictive, prescriptive, optimization, anomaly detection, and simulation models that improve cost, service, and resilience across planning, logistics, and operations.
You will partner with data engineering, AI engineering, and business stakeholders to translate problems into deployable solutions-delivering decision signals that are embedded into operational workflows, planning systems, and agentic experiences.
Responsibilities include but not limited to:
  • Build predictive models for key supply chain processes using statistical, machine learning, and deep learning techniques
  • Develop prescriptive analytics and optimization models to recommend optimal actions under real-world constraints
  • Quantify tradeoffs between cost, service, capacity, and risk
  • Detect variability, disruptions, and anomalies across supply chain operations
  • Build simulations and scenario models to support strategic and operational decisions
  • Partner with stakeholders to translate business problems into data science solutions
  • Enable AI and agentic workflows by producing high-quality predictive and prescriptive signals
  • Merge and analyze large, complex datasets to discover trends, patterns, and actionable insights

Basic Qualifications:
  • Master's degree in data science, statistics, computer science or related field
  • 8+ years of professional experience, including 2+ years in supply chain analytics (planning, forecasting, logistics, manufacturing, or operations)
  • Strong proficiency in Python and SQL
  • Proven experience with predictive modeling (statistical, ML, deep learning), optimization techniques (LP, MIP, constraint programming), and simulation techniques (Monte Carlo, discrete event)
  • Knowledge of advanced statistical techniques and concepts (regression, distributions, statistical tests)
  • Familiarity with a variety of machine learning techniques (clustering, decision trees, neural networks) and their real-world advantages and limitations
  • Experience with data manipulation libraries such as pandas and NumPy
  • Experience with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow
  • Knowledge of big data frameworks and platforms such as Spark, Databricks, or Snowflake

Preferred Qualifications:
  • PhD
  • Experience with supply chain planning, forecasting, or logistics datasets
  • Experience with Databricks, Spark, Snowflake or Palantir Foundry & AIP
  • Experience with MLOps practices (model monitoring, CI/CD for ML)

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