1

Data Science Jobs in Columbus, OH (NOW HIRING)

AI and Data Science Engineer II

Columbus, OH · On-site

$110K - $132K/yr

We are a team of strategists, data scientists, operators, creatives, designers, engineers, and architects. Our team balances business strategy, technology, creativity, and ongoing managed services to ...

You will be joining a team of analytics and data science professionals within the Enterprise Data & Analytics organization. Team members are highly skilled in a mixture of R, Python, SAS, SQL ...

The Hartford is expanding its Data Science, AI & Analytics capabilities to deliver nextgeneration AI, Generative AI, and Agentic AI solutions across underwriting, claims, risk analysis, and ...

Sr. Data Scientist, Payments

Delaware, OH · On-site

$152K - $205K/yr

Create data visualizations to translate analytic and data science results for broad understanding across the business * Build scalable automation solutions using SQL, dashboards, and other tools to ...

next page

Showing results 1-20

Data Science information

See Columbus, OH salary details

$36.2K

$118.6K

$189.8K

How much do data science jobs pay per year?

As of Jun 11, 2026, the average yearly pay for data science in Columbus, OH is $118,553.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,100.00 and $131,400.00 per year, depending on experience, location, and employer.

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.

Is 40 too late for data science?

Data science is a field open to individuals of all ages, and many professionals transition into it later in their careers. Success often depends on acquiring relevant skills such as programming, statistics, and machine learning, which can be learned through online courses, bootcamps, or degrees regardless of age.

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.

Is AI replacing data scientists?

AI is transforming the role of data scientists by automating routine tasks such as data cleaning and basic analysis, but it does not replace the need for skilled professionals to interpret complex data, develop models, and make strategic decisions. Data scientists with expertise in programming, statistical analysis, and machine learning remain essential for designing and deploying AI solutions effectively.

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 jobs are there in data science?

Data science offers a variety of roles including Data Scientist, Data Analyst, Machine Learning Engineer, Data Engineer, and Business Intelligence Analyst. These positions typically require skills in programming, statistics, and data visualization tools, and may involve working with large datasets, predictive modeling, and data-driven decision making.

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.

What jobs does a data scientist do?

A data scientist analyzes large datasets to extract insights, build predictive models, and support decision-making. They use programming languages like Python or R, employ statistical techniques, and often work with machine learning algorithms to solve complex problems across various industries.
What are the most commonly searched types of Data Science jobs in Columbus, OH? The most popular types of Data Science jobs in Columbus, OH are:
What are popular job titles related to Data Science jobs in Columbus, OH? For Data Science jobs in Columbus, OH, the most frequently searched job titles are:
What cities near Columbus, OH are hiring for Data Science jobs? Cities near Columbus, OH with the most Data Science job openings:

Lead Data Scientist - US Remote

Hexion Careers

Columbus, OH • On-site, Remote

Full-time

Posted 17 days ago


Job description

Company Overview
 

Imagine Everything. Build the Future with Hexion.

At Hexion, we push boundaries, rethink possibilities, and create real impact. We activate science to deliver progress—developing breakthrough solutions that strengthen industries, protect communities, and drive a more sustainable future.

This is where bold thinkers, problem-solvers, and innovators come together to shape what’s next. Whether you're engineering advanced materials, transforming manufacturing technologies, or leading strategic innovation, your ideas and actions leave a lasting mark. We cultivate an inclusive culture of growth, collaboration, and accountability, ensuring every contribution propels us forward.

We don’t follow the status quo—we challenge it, disrupt it, and improve it. Every role at Hexion is part of something bigger.

We invest in innovation, sustainability, and continuous development—equipping you with the tools, training, and opportunities to excel. With an unwavering commitment to safety, partnership, belonging, and impact, we empower you to lead change and strengthen industries worldwide.

Your Future Starts Here.  

If you’re ready to push limits, reimagine what’s possible, and create the extraordinary, Hexion is where you belong. 

Anything is possible when you imagine everything. 

Job Responsibilities
  • Lead complex data science and machine learning initiatives supporting supply chain, manufacturing operations, capacity planning, demand forecasting, and operational decision-making. 
  • Design, develop, and own advanced ML solutions — including predictive models, time-series forecasting, optimization, and decision-support systems — scoped to supply chain and manufacturing use cases. 
  • Build, train, evaluate, and interpret machine learning models (regression, classification, clustering, forecasting) to quantify supply chain drivers, surface optimization opportunities, and improve operational outcomes. 
  • Develop and operationalize analytics and ML solutions using Databricks (Python / SQL / PySpark) for large-scale data processing, model development, and experimentation. 
  • Design and build multi-agent AI systems — including orchestrator-executor architectures, tool-calling agents, and RAG-based decision support — using frameworks such as Azure AI Foundry, AutoGen, Semantic Kernel, or LangChain/LangGraph. 
  • Implement and extend solutions using the MCP to enable AI agents to access and act on enterprise data systems in supply chain and manufacturing contexts. 
  • Apply data science best practices including feature engineering, model validation, performance monitoring, reproducibility, and documentation. 
  • Partner with Supply Chain & Procurement leadership, Manufacturing Ops, Process Engineering, Demand Planning, and IT to translate ambiguous business problems into structured ML and AI approaches. 
  • Develop and maintain self-service, automated, and AI-enabled analytics workflows that reduce manual effort and improve decision latency. 
  • Leverage Azure AI Foundry, Microsoft Copilot Studio, and Microsoft 365 Copilot extensibility to prototype and deploy AI-powered analytics and agent-based decision-support tools. 
  • Produce executive-ready insights through clear storytelling, visualizations, and recommendations using Power BI or embedded analytics. 
  • Set technical direction, establish reusable ML and AI frameworks, and mentor junior and mid-level data scientists across the team. 
  • Ensure high standards of data quality, governance, model validation, and explainability. 
Minimum Qualifications

Education & Experience (one of the following): 

  • Master’s degree in Statistics, Mathematics, Industrial Engineering, Data Science, Computer Science, Engineering, or a related quantitative field with 5+ years of relevant data science/analytics experience, OR 
  • Bachelor’s degree in the same or related fields with 8+ years of relevant data science / analytics experience. 


Technical: 

  • Demonstrated track record delivering advanced ML and data science solutions in supply chain, manufacturing, or industrial domains. 
  • Strong hands-on experience with machine learning and statistical modeling — development, interpretation, and operational business application. 
  • Strong proficiency in Databricks (Python, SQL, PySpark, Delta Lake). 
  • Hands-on experience with the MCP — building or consuming MCP servers/clients to connect AI agents to enterprise data systems, APIs, or ERP modules. 
  • Hands-on experience with multi-agent system design — architecting multi-agent systems using AutoGen, Semantic Kernel, LangChain/LangGraph, or Azure AI Agent Service; orchestrator-executor patterns, tool calling, memory management, and agent coordination. 
  • Compulsory — must have hands-on experience with one or more of the following: 
    • Azure AI Foundry 
    • Microsoft Copilot Studio 
    • Microsoft 365 Copilot extensibility 
    • Microsoft Power Platform (Power Automate, Power BI) 
  • Ability to translate complex business problems into ML / AI solutions and communicate findings to both technical and executive audiences. 
  • Strong stakeholder management and cross-functional collaboration skills. 
Preferred Qualifications
  • Experience operationalizing ML models into production in supply chain or manufacturing environments. 
  • Familiarity with SAP ECC / S/4HANA supply chain and manufacturing modules (MM, PP, PM, SD). 
  • Strong Power BI experience — semantic modeling, performance optimization, executive dashboard design. 
  • Exposure to MLOps on Azure (Azure ML, MLflow, Databricks Asset Bundles, CI/CD for analytics artifacts). 
  • Experience designing operational KPI frameworks (MAPE, OTIF, service level, OEE, downtime). 
  • Experience with statistical / simulation methods (Monte Carlo, scenario analysis, sensitivity analysis) applied to operations and supply chain. 
  • Familiarity with Palantir Foundry (pipelines, ontology, Workshop, AIP). 
  • Proven experience mentoring data scientists or leading end-to-end analytics initiatives. 
  • Familiarity with cloud-native data architectures and governed data platforms. 
Other
 

We are an Equal Opportunity, Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to gender, pregnancy, race, national origin, religion, age, sexual orientation, gender identity, veteran or military status, status as a qualified individual with a disability or any other characteristic protected by law.

To be considered for this position candidates are required to submit an application for employment through our career site and, be at least 18 years of age.  Any offer of employment will be conditioned upon successful completion of a drug test and background investigation, as well as authorization for the Company to conduct additional periodic background checks as required by the Chemical Facility Anti-Terrorism Standards (CFATS) or regulations adopted by the department of Homeland Security or other regulatory agencies. A prior criminal record is not an automatic bar to employment, and the Company will conduct an individualized assessment and reassessment, consistent with applicable law, prior to making any final employment decision.