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Data Science Jobs in Indiana (NOW HIRING)

USM

Data Scientist

Columbus, IN

Data Scientist Location: Columbus, IN Duration: 9 weeks Primary Skills: this is a fixed fee project ... Education Requirements MINIMUM MSc in Statistics, Mathematics, Computer Science; PhD preferred ...

Lilly

Data Architect, Data Foundry

Indianapolis, IN · On-site

Architecture4Insight (data infrastructure and scientific software), Methods4Insight (analytical and computational methods), Automation & Scale4Insight (lab automation and agentic workflows), and ...

NiSource

Manager Data and Analytics

Merrillville, IN

$133K - $200K/yr

Oversees activities related to an internal data and analytics portfolio (may include data science, data engineering, data prep, data governance, data stewardship, visualization, alerting and ...

NiSource

Manager Data and Analytics

Merrillville, IN · On-site

$133K - $200K/yr

Oversees activities related to an internal data and analytics portfolio (may include data science, data engineering, data prep, data governance, data stewardship, visualization, alerting and ...

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All JobsData Science JobsData Science Jobs in Indiana

Data Science information

See Indiana salary details

$35.7K

$116.8K

$187K

How much do data science jobs pay per year?

As of Jun 19, 2026, the average yearly pay for data science in Indiana is $116,793.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,700.00 and $129,400.00 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.
More about Data Science jobs
What are the most commonly searched types of Data Science jobs in Indiana? The most popular types of Data Science jobs in Indiana are:
What are popular job titles related to Data Science jobs in Indiana? For Data Science jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Data Science jobs in Indiana look for? The top searched job categories for Data Science jobs in Indiana are:
What cities in Indiana are hiring for Data Science jobs? Cities in Indiana with the most Data Science job openings:
Data Science jobs near you
Infographic showing various Data Science job openings in Indiana as of June 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $116,793 per year, or $56.2 per hour.
Postdoctoral Fellow in Biostatistics and Health Data Science

Postdoctoral Fellow in Biostatistics and Health Data Science

Indiana University

Bloomington, IN • On-site

$45K - $61K/yr

Full-time

Posted 28 days ago

Job description

Posting Details
Position Details
Title
Postdoctoral Fellow in Biostatistics and Health Data Science
Specific Title
Appointment Type
Postdoctoral Fellow
Department
IUSM - Biostatistics
Campus
IU School of Medicine Indianapolis
Position Summary
Postdoctoral Researcher to advance research at the intersection of artificial intelligence for healthcare, multimodal data analysis (EHRs, medical imaging, omics, physiological signals, clinical notes), and causal AI (causal inference, discovery, counterfactual reasoning). The successful candidate will collaborate with an interdisciplinary team of computer scientists, biomedical informaticians, clinicians, and public health researchers to develop deployable, trustworthy methods that improve patient outcomes and health system operations.
Key responsibilities include:
  • Lead original research in multimodal and causal AI for health; design, implement, and rigorously evaluate algorithms and full pipelines.
  • Build reproducible research pipelines and maintain reliable experiment codebases (prefer Python).
  • Apply causal inference and discovery frameworks to clinical questions.
  • Translate proposed methods and frameworks into real-world clinical workflows.
  • Contribute to grant proposals and research reports.

This is an exciting opportunity to join a fast growing BHDS Department with 36 faculty members and more than 50 professional staff. We are dedicated to excellence in biostatistical, health data science, and informatics research and education. We have an extensive portfolio of research program in related areas. Our NIH funding as principal investigators and co-investigators has been ranked among the top in the nation. The Department currently has an ongoing PhD program in Biostatistics, MS program in Biostatistics, and BS program in Health Data Science.
We have established strong collaborations with both clinical and basic science research departments and divisions and health systems including IU Health and Eskenazi Hospitals as well as strong partnerships with major research institutes and centers including Regenstrief Institute, Center for Computational Biology and Bioinformatics, Indiana Clinical and Translational Science Institute (CTSI), Indiana University Simon Comprehensive Cancer Center, Indiana Institute of Biomedical Research, and Indiana Alzheimer's Disease Research Center. Additional details about the Department and the PhD program are available on our web page: https://medicine.iu.edu/biostatistics.
The Indianapolis Campus is the focal point of health professions education at Indiana University, and the School of Medicine is the country's second largest allopathic medical school. Indianapolis consistently ranks high nationally on many of the "best places to live" lists and has an economy that is growing in the life sciences arena. In addition, it has always been one of the cities with the lowest cost of living. Carmel, Indy's northern neighbor, was recently named as the best mid-sized city in the country.
IUSM is committed to being a welcoming campus community and we seek candidates whose research, teaching, and community engagement efforts contribute to robust learning and working environments for all students, staff, and faculty. We invite individuals who will join us in our mission to improve health equity and well-being for all throughout the state of Indiana.
Indianapolis is the capital and most populous city in the State of Indiana. It is growing economically thanks to a strong corporate base anchored by the life sciences. Indiana is home to one of the largest concentrations of health sciences companies in the nation. Indianapolis has a sophisticated blend of charm and culture with a wonderful balance of business and leisure. The growing residential base is supported by rich amenities and quality of life - the city possesses a variety of professional sports, arts venues and outdoor recreation areas. Residents of this dynamic city, and surrounding suburbs, enjoy leading educational systems and top-ranked universities, paired with a diverse population. Indianapolis International Airport is a top-ranked international airport, being named "Best Airport in North America" by Airports Council International for many years. For additional information on life in Indy: https://faculty.medicine.iu.edu/relocationThe search will continue until the positions are filled.
Basic Qualifications
Required Qualifications:
  • Ph.D. (by start date) in Computer Science, Biomedical Informatics, Health Data Science, Biostatistics, or a closely related area.
  • Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP.
  • Demonstrated working experience with healthcare data (e.g., EHR, clinical text, imaging, omics).
  • Proficiency in Python and ML tooling (e.g., PyTorch, scikit-learn), version control (Git), and experiment tracking (e.g., Weights & Biases).
  • Excellent written and oral communication skills, and ability to collaborate with multidisciplinary teams.

Preferred Qualifications:
  • Experience with LLMs/foundation models (e.g., clinical NLP, retrieval-augmented generation, instruction tuning) and multimodal transformers.
  • Solid understanding of causal methods (e.g., propensity scores, IPW, matching) and/or causal discovery.
  • Familiarity with data engineering and MLOps (e.g., SQL, Spark, Airflow, Docker, Kubernetes).
  • Knowledge of responsible/ethical AI for health: fairness/equity, interpretability, robustness, privacy (e.g., differential privacy, federated learning).
  • Track record of first-author publications in relevant venues and collaborative open-source contributions.

Department Contact for Questions
Professor Jiang Bian via email at: bianj@regenstrief.org
Additional Qualifications
Special Instructions
Priority Application Review Deadline
Expected Start Date
Posting Number
IUSM-02286-2025

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