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Scientific Machine Learning Jobs in Indiana (NOW HIRING)

Elevance Health

Research Scientist Senior

Indianapolis, IN · On-site

$94K - $120K/yr

... machine learning and reinforcement learning systems that optimize healthcare and operational decision-making. How you will make an impact: * Leads the assessment of scientific research that ...

Elevance Health

Research Scientist Senior

Indianapolis, IN · On-site +1

$94K - $120K/yr

... machine learning and reinforcement learning systems that optimize healthcare and operational decision-making. How you will make an impact: * Leads the assessment of scientific research that ...

Elevance Health

Research Scientist Senior

Indianapolis, IN · On-site +1

$94K - $119K/yr

... machine learning and reinforcement learning systems that optimize healthcare and operational decision-making. How you will make an impact: * Leads the assessment of scientific research that ...

Indiana University

Research Scientist

Bloomington, IN · On-site

$70K - $75K/yr

There is both a scientific premise and a methodological premise underlying the project. The ... machine learning and graph theoretics, one can discover multiple developmental pathways in cross ...

Deloitte

Data Scientist

Indianapolis, IN · On-site

As an experienced Data Scientist, you will have the ability to share new ideas and collaborate on ... Develop and train machine learning models to solve problems such as prediction, classification, and ...

ClinLab Solutions Group

IN

$61 - $78.25/hr

Collaborate with business analysts, scientists, and AI engineers to develop and deploy machine learning, AI, mechanistic, statistical, and hybrid models. * Iterate dynamically with end users and ...

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All JobsScientific Machine Learning JobsScientific Machine Learning Jobs in Indiana

Scientific Machine Learning information

Is ML a high paying job?

Scientific Machine Learning roles typically offer high salaries due to the specialized skills required, such as expertise in deep learning, data analysis, and programming with tools like Python and TensorFlow. Compensation varies by industry, experience, and location but generally exceeds average tech salaries for comparable roles.

Which 3 jobs will survive AI?

Scientific Machine Learning professionals will likely continue to be in demand due to their expertise in developing and applying AI models to complex scientific problems. Roles such as data scientists, AI researchers, and machine learning engineers are expected to persist because they require specialized knowledge, critical thinking, and ongoing innovation that AI tools complement rather than replace. These jobs often involve interdisciplinary skills, programming, and understanding of domain-specific data, making them more resilient to automation.

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are some common challenges faced by professionals in Scientific Machine Learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

How much does a machine learning scientist make?

A machine learning scientist typically earns between $90,000 and $150,000 annually, depending on experience, education, and location. Senior roles or those with specialized skills in deep learning or natural language processing can earn higher salaries, often exceeding $180,000.

What are the key skills and qualifications needed to thrive as a Scientific Machine Learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

Is 40 too late for data science?

Scientific Machine Learning roles often value skills and experience over age, and many professionals transition into data science or machine learning at various stages of their careers. Learning relevant tools like Python, TensorFlow, or scikit-learn and gaining practical experience can help regardless of age, making 40 not too late to pursue this field.
What are popular job titles related to Scientific Machine Learning jobs in Indiana? For Scientific Machine Learning jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in Indiana look for? The top searched job categories for Scientific Machine Learning jobs in Indiana are:
What cities in Indiana are hiring for Scientific Machine Learning jobs? Cities in Indiana with the most Scientific Machine Learning job openings:
Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Indiana as of June 2026, with employment types broken down into 78% Full Time, and 22% Part Time. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution.
Computational Chemistry/Toxicology Scientist

Computational Chemistry/Toxicology Scientist

Vish Consulting Services Inc

Zionsville, IN • On-site

Contractor

Posted 25 days ago

Job description

VCS is looking for a computational chemistry/Toxicology Scientist for one of our client

Job Title - Computational Chemistry/Toxicology Scientist

Job Location - Indianapolis, IN 46077

Contract- 12+ Months with possible extension

Shift- Day M-F 40 hrs./week

Pay- 57/Hr.

Description:
The client has an exciting and challenging opportunity in the Predictive Safety Center within Regulatory Science for a Computational Scientist Contractor with expertise in c/Bioinformatics. The individual will partner with a multidisciplinary team to design, develop, and implement machine learning models to predict safety profiles of chemicals to support the discovery, development and registration of novel crop protection products. This position is located in Indianapolis, IN, while strong candidates working remotely will be considered.
Responsibilities:

  • Develop and apply chemistry structure-based predictive models for assessing safety profiles of early-stage discovery molecules
  • Assess potential mechanisms of toxicity by assessing ligand-protein binding affinity using protein structure alignment, binding pocket evaluation, and molecular docking
  • Work collaboratively with internal and external cross-functional multidisciplinary teams, collaborators, and consultants to implement in silico models to meet business needs
  • Serve as a subject matter expert on in silico modelling for the team and other partners in R&D sub-functions
  • Present the development and application of cheminformatics/bioinformatics approaches and novel ML/DL models internally and externally including scientific conferences
  • Keep abreast of the latest scientific development in the areas of machine learning, cheminformatics, bioinformatics, and related fields and identify technologies to be applied internally
  • Analyze complex datasets using machine learning approaches and interpret results to improve data-driven decision-making processes

Qualifications:

  • Ph.D. degree in Cheminformatics, Computational Chemistry, Computational Biology, Bioinformatics, Toxicology, or a related field
  • 3+ years of experience in developing machine learning models and applying cheminformatics/bioinformatics and AI for structure-based drug design, toxicology assessment, mechanism prediction
  • Strong technical background in machine learning, deep learning, and statistical modeling with prior experience in applying these techniques to process, analyze and draw insights from complex datasets involving chemical compound structures and toxicity endpoints
  • Demonstrated programming proficiency in Python and experience in utilizing machine learning libraries
  • Demonstrated in-depth knowledge in toxicology, chemistry, chemical structure, QSAR, protein structure
  • Knowledge in omics analysis and systems biology is a plus
  • Demonstrated teamwork and project leadership skills to manage multiple projects on different timelines for stakeholders across the business
  • Excellent communication and presentation skills to different stakeholder audiences

Thank You!

Bhanendra S Singh
Vish Consulting Services(VCS)
Bhanendra @vishusa.com