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Data Science Jobs in Indiana (NOW HIRING)

Norstella

Senior Data Scientist

Indianapolis, IN · Remote

Job Summary Our dedicated Data Science team is at the forefront of revolutionizing pharma intelligence and how patients gain access to life-saving therapies. Armed with cutting-edge technology and a ...

Toptal

This is a founding role: you will shape the data science function from the ground up, set technical direction, and own the end-to-end delivery of intelligent systems that define how our product ...

SynergisticIT

Junior Data Scientist

Indianapolis, IN · On-site

Who Should Apply Recent Computer Science/Engineering /Mathematics/Statistics or Science Graduates ... data Science/Machine learning Positions REQUIRED SKILLS Bachelors degree or Masters degree in ...

SynergisticIT

Data Scientist - Junior

Indianapolis, IN · On-site

Who Should Apply Recent Computer Science/Engineering /Mathematics/Statistics or Science Graduates ... data Science/Machine learning Positions REQUIRED SKILLS Bachelors degree or Masters degree in ...

Sedgwick

Principal Data Scientist

Indianapolis, IN · On-site +1

Master's or PhD in Data Science, Statistics, Mathematics, Computer Science, Economics, or related quantitative discipline. * 8-12+ years of experience in data science, statistical modeling, or ...

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How much do data science jobs pay per year?

As of May 28, 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 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 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.

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.

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.

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

Data Scientist

Recruitment Events Calendar

Indianapolis, IN • On-site

$125.78K/yr

Other

This job post has expired today. Applications are no longer accepted.

Job description

Reemployed annuitant: This vacancy does not meet the criteria for appointment of annuitants.Qualifications:All qualification and time-in-grade (if applicable) requirements must be met within 30 days of the closing date of this announcement.
You must meet the minimum qualification requirements as stated in the Office of Personnel Management (OPM) Operating Manual, Qualification Standards for General Schedule Positions, http://www.opm.gov/qualifications/Standards/group-stds/gs-prof.asp.
Basic Requirement:

  1. Degree: Mathematics, statistics, computer science, data science or field directly related to the position. The degree must be in a major field of study (at least at the baccalaureate level) that is appropriate for the position.
    OR

  2. Combination of education and experience: Courses equivalent to a major field of study (30 semester hours) as shown in paragraph A above, plus additional education or appropriate experience.

Specialized Experience:GS-14 - In addition to meeting the basic requirement you must have one year of specialized experience at the GS-13 or equivalent level. Specialized experience must be documented in your resume.
Specialized experience is defined as: directing and conducting complex data science and data preparation activities on large structured, semi-structured, and unstructured datasets, including designing and optimizing data integration processes, transformations, and data structures that enable analytics, automation, and machine learning use cases; OR applying advanced quantitative, machine learning, or predictive techniques to develop innovative, data-driven solutions that improve operational analytics and support critical business and audit decisions; OR leading the full lifecycle of data-centric projects, from defining objectives and recommending data collection, integration, and retention requirements, to presenting findings and providing authoritative policy recommendations to senior leadership; OR providing input to and supporting enterprise data governance frameworks, including data classification, lineage, metadata cataloging, and integration with enterprise data and governance platforms, using languages such as SQL, Python, or R and modern data platform tools.
Volunteer Experience: Experience refers to paid and unpaid experience, including volunteer work done through National Service programs (e.g., Peace Corps, AmeriCorps) and other organizations (e.g., professional, philanthropic, religious, spiritual, community, student, social). Volunteer work helps build critical competencies, knowledge, and skills, and can provide valuable training and experience that translates to paid employment. You will receive credit for all qualifying experience, including volunteer experience.Education:Substitution of Education for Specialized Experience: There is no substitution of education for experience at this grade level.Employment Type: OTHER

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