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Data Scientist Supply Chain Jobs (NOW HIRING)

Auger

Data Scientist - Supply Chain

Bellevue, WA · On-site

They are seeking a Data Scientist to transform raw customer data into structured insights, working closely with supply chain experts to develop AI and optimization systems for complex supply chain ...

Anthropic

Data Scientist, Supply

San Francisco, CA · On-site

... the supply narrative shared with the CTO and staff Minimum qualifications * Strong technical individual-contributor background in data science, analytics, or operations research * Demonstrated ...

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Data Scientist Supply Chain information

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$165K

$243.5K

How much do data scientist supply chain jobs pay per year?

As of Jul 26, 2026, the average yearly pay for data scientist supply chain in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Data Scientist Supply Chain position, and why are they important?

To thrive as a Data Scientist Supply Chain, you need strong analytical and statistical skills, proficiency in machine learning, data modeling, and a relevant degree such as in data science, engineering, mathematics, or supply chain management. Familiarity with programming languages like Python or R, experience with SQL, and working knowledge of supply chain management systems (such as SAP or Oracle) and certifications like APICS or Six Sigma are often valuable. Excellent problem-solving abilities, effective communication, and the capacity to collaborate with cross-functional teams are crucial soft skills for this role. These abilities enable the data scientist to deliver actionable insights, optimize logistics operations, and drive efficiency within complex supply chain environments.

What is a Data Scientist Supply Chain job?

A Data Scientist in Supply Chain leverages data analytics, machine learning, and statistical modeling to optimize processes such as demand forecasting, inventory management, and logistics. They work with large datasets to uncover insights that improve efficiency, reduce costs, and enhance decision-making. Their role often involves collaborating with cross-functional teams, developing predictive models, and automating data-driven solutions to enhance supply chain performance.

Can you make 200k a year in supply chain?

Data scientists working in supply chain roles can potentially earn $200,000 or more annually, especially with seniority, specialized skills, or in high-demand industries. Achieving this salary often requires advanced analytics expertise, experience with tools like Python or R, and a strong understanding of supply chain processes. Salary levels vary based on location, company size, and individual qualifications.

Is 40 too late for data science?

Age is not a barrier to becoming a data scientist in the supply chain field. Many professionals transition into data science later in their careers by acquiring relevant skills such as programming, statistics, and tools like Python or R, often through online courses or certifications. Success depends on your skills, experience, and ability to adapt to new technologies, regardless of age.

What is a supply chain data scientist?

A supply chain data scientist analyzes data related to logistics, inventory, and procurement to optimize supply chain operations. They use statistical models, machine learning, and data visualization tools to identify inefficiencies and improve decision-making processes within supply chains.

Can data scientists make $300K?

Data scientists in supply chain roles can earn $300K or more, especially with extensive experience, advanced skills in machine learning and analytics, and leadership responsibilities. High salaries are often found in senior positions, specialized industries, or companies with competitive compensation packages.

What are the typical daily responsibilities of a Data Scientist in the supply chain sector?

A Data Scientist in supply chain typically spends their days analyzing large sets of logistics, inventory, and sales data to identify trends, forecast demand, and optimize various processes like procurement, warehousing, and distribution. They collaborate with operations managers, IT specialists, and business leaders to translate data-driven insights into actionable strategies and performance improvements. Using advanced analytics tools and visualization platforms, they regularly build predictive models and share their findings with both technical and non-technical stakeholders. This role often involves a mix of solo analytical work and teamwork, offering opportunities to impact key business decisions and drive measurable operational efficiencies.

More about Data Scientist Supply Chain jobs
What cities are hiring for Data Scientist Supply Chain jobs? Cities with the most Data Scientist Supply Chain job openings:
What are the most commonly searched types of Data Scientist Supply Chain jobs? The most popular types of Data Scientist Supply Chain jobs are:
What states have the most Data Scientist Supply Chain jobs? States with the most job openings for Data Scientist Supply Chain jobs include:
What job categories do people searching Data Scientist Supply Chain jobs look for? The top searched job categories for Data Scientist Supply Chain jobs are:
Data Scientist Supply Chain jobs near you
Infographic showing various Data Scientist Supply Chain job openings in the United States as of July 2026, with employment types broken down into 89% Full Time, 8% Part Time, 2% Contract, and 1% Nights. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.
Data Scientist - Supply Chain

Data Scientist - Supply Chain

Stellantis

Auburn Hills, MI • On-site

Full-time

Posted 11 days ago

Stellantis rating

7.4

Company rating: 7.4 out of 10

Based on 129 frontline employees who took The Breakroom Quiz

18th 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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