OPT Data Scientist Jobs
Data Scientist roles are among the most actively sponsored positions for F-1 OPT students, with strong demand across tech, finance, and healthcare. Most positions qualify under STEM OPT, giving you up to three years of work authorization. A background in machine learning, Python, or statistical modeling significantly strengthens your candidacy.
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Get Access To All JobsINTRODUCTION
The Data Scientist brings deep expertise in classical AI/ML, statistics, and advanced analytics to Fairview Health Services’ AI & Automation program. The role independently frames complex problems, develops and validates data science approaches, builds solutions, and assesses models and methods developed by internal teams and external partners.
Working with clinical and operational stakeholders, engineering teams, and governance partners, the Data Scientist supports technical intake, feasibility assessment, solution design, implementation, and ongoing scientific review. The role determines whether an approach is technically sound and appropriate for its intended use, identifies limitations, recommends corrections, and communicates evidence-based technical feedback. Generative AI and LLM capabilities complement the role’s core statistical and machine-learning expertise.
This role will serve as a primary scientific and technical resource for internally developed and partner-delivered AI/ML solutions. The position will support model development, independent validation, methodology review, experimentation, and technical challenge while helping establish how Fairview evaluates model performance, limitations, bias, and ongoing scientific validity.
Responsibilities
Intake and solution design. Translate clinical and operational challenges into testable questions, data requirements, scientific approaches, and measures of success. Assess technical feasibility and recommend suitable analytical or AI/ML approaches for internal and partner initiatives.
Model development and review. Develop and validate statistical and machine-learning models, or independently review and approve models developed by others. MLOps owns production deployment and operation; partner with MLOps to transition validated models into supported use. Illustrative use cases include risk prediction, readmissions, population health, forecasting, and capacity planning.
Scientific methods validation. Design reproducible analyses, experiments, and validation plans. Assess data quality, feature selection, assumptions, validation design, performance, and limitations to determine whether results are reliable and support the proposed use.
Partner and internal assurance. Review model methods, code, data assumptions, validation evidence, and reported results delivered by internal teams and external partners. Reproduce or challenge findings as needed and recommend corrections or additional evidence.
Scientific review and approval. Independently review and approve model methods and validation evidence within assigned scientific-review authority and established governance. Document acceptance criteria, findings, limitations, and remediation; coordinate clinical, security, and production-release decisions with designated owners.
Applied data science. Develop and validate approaches to clinical and operational problems using linear and other regression models, random forests, and other suitable methods. Apply statistics to evaluate interventions, programs, and workflows; use optimization, natural-language processing, generative AI, and large language models where appropriate.
Data and implementation partnership. Define feature, cohort, outcome, and data requirements with the AI Data Modeler, data engineering, and domain owners. Build reproducible analytical code and prototypes and partner with MLOps and IT teams to implement validated solutions in supported workflows.
Ongoing scientific validity. Define model-performance measures and review criteria with scientific and domain stakeholders. Interpret monitoring findings and recommend or develop recalibration, retraining, or methodological changes; partner with MLOps on controlled implementation.
Technical leadership and communication. Independently lead the scientific work on assigned initiatives. Explain results and limitations to clinical, operational, technical, and partner audiences; provide constructive pushback, mentor colleagues, and maintain clear methodological documentation.
Privacy and governance. Follow organizational privacy, security, regulatory, and model-governance requirements. Provide documented scientific evidence supporting responsible use and informed clinical and operational decisions.
BASIC QUALIFICATIONS
- B.S./B.A. in a quantitative field such as Statistics, Computer Science, Engineering, Applied Mathematics, Data Science, Epidemiology, or Health Informatics; or a combination of education and experience equivalent to a bachelor’s degree in a quantitative field.
- Four years of data science, predictive-modeling, or advanced-analytics experience, including independent responsibility for complex analytical or scientific work.
- Demonstrated proficiency in analytical programming, advanced analytics and statistics, predictive modeling, machine learning, and data visualization.
- Strong proficiency in Python and/or R and experience working with large relational databases using SQL.
- Demonstrated experience developing and validating models or independently reviewing and validating models developed by others. Either experience route must demonstrate hands-on ability to implement analytical methods, test results, and critically assess scientific evidence.
- Deep knowledge of statistical reasoning, linear and other regression techniques, random forests, supervised and unsupervised learning, feature engineering, forecasting, model evaluation, and the selection and validation of data science methods.
- Ability to independently assess ambiguous problems, identify methodological limitations, recommend corrections, and communicate scientific and technical findings clearly.
- Ability to work effectively with clinical, operational, engineering, and external-partner teams; provide constructive technical feedback; manage multiple priorities; and support implementation of reproducible solutions.
PREFERRED QUALIFICATIONS
- M.A./M.S. in a related quantitative field.
- Experience with healthcare/EHR data, including Epic Clarity or Caboodle, and clinical-quality or operational measures.
- Experience with outcomes research, clinical research, population health, causal inference, optimization, or evaluation of interventions and workflows.
- Experience developing or evaluating natural-language processing, generative AI, or large language model solutions.
- Experience with cloud-based development, containerized solutions, computational infrastructure, modern data platforms, and data visualization.
- Knowledge of healthcare regulatory and reimbursement environments.
COMPENSATION
- $114,857.60 - $162,177.60 Annual
INTRODUCTION
The Data Scientist brings deep expertise in classical AI/ML, statistics, and advanced analytics to Fairview Health Services’ AI & Automation program. The role independently frames complex problems, develops and validates data science approaches, builds solutions, and assesses models and methods developed by internal teams and external partners.
Working with clinical and operational stakeholders, engineering teams, and governance partners, the Data Scientist supports technical intake, feasibility assessment, solution design, implementation, and ongoing scientific review. The role determines whether an approach is technically sound and appropriate for its intended use, identifies limitations, recommends corrections, and communicates evidence-based technical feedback. Generative AI and LLM capabilities complement the role’s core statistical and machine-learning expertise.
This role will serve as a primary scientific and technical resource for internally developed and partner-delivered AI/ML solutions. The position will support model development, independent validation, methodology review, experimentation, and technical challenge while helping establish how Fairview evaluates model performance, limitations, bias, and ongoing scientific validity.
Responsibilities
Intake and solution design. Translate clinical and operational challenges into testable questions, data requirements, scientific approaches, and measures of success. Assess technical feasibility and recommend suitable analytical or AI/ML approaches for internal and partner initiatives.
Model development and review. Develop and validate statistical and machine-learning models, or independently review and approve models developed by others. MLOps owns production deployment and operation; partner with MLOps to transition validated models into supported use. Illustrative use cases include risk prediction, readmissions, population health, forecasting, and capacity planning.
Scientific methods validation. Design reproducible analyses, experiments, and validation plans. Assess data quality, feature selection, assumptions, validation design, performance, and limitations to determine whether results are reliable and support the proposed use.
Partner and internal assurance. Review model methods, code, data assumptions, validation evidence, and reported results delivered by internal teams and external partners. Reproduce or challenge findings as needed and recommend corrections or additional evidence.
Scientific review and approval. Independently review and approve model methods and validation evidence within assigned scientific-review authority and established governance. Document acceptance criteria, findings, limitations, and remediation; coordinate clinical, security, and production-release decisions with designated owners.
Applied data science. Develop and validate approaches to clinical and operational problems using linear and other regression models, random forests, and other suitable methods. Apply statistics to evaluate interventions, programs, and workflows; use optimization, natural-language processing, generative AI, and large language models where appropriate.
Data and implementation partnership. Define feature, cohort, outcome, and data requirements with the AI Data Modeler, data engineering, and domain owners. Build reproducible analytical code and prototypes and partner with MLOps and IT teams to implement validated solutions in supported workflows.
Ongoing scientific validity. Define model-performance measures and review criteria with scientific and domain stakeholders. Interpret monitoring findings and recommend or develop recalibration, retraining, or methodological changes; partner with MLOps on controlled implementation.
Technical leadership and communication. Independently lead the scientific work on assigned initiatives. Explain results and limitations to clinical, operational, technical, and partner audiences; provide constructive pushback, mentor colleagues, and maintain clear methodological documentation.
Privacy and governance. Follow organizational privacy, security, regulatory, and model-governance requirements. Provide documented scientific evidence supporting responsible use and informed clinical and operational decisions.
BASIC QUALIFICATIONS
- B.S./B.A. in a quantitative field such as Statistics, Computer Science, Engineering, Applied Mathematics, Data Science, Epidemiology, or Health Informatics; or a combination of education and experience equivalent to a bachelor’s degree in a quantitative field.
- Four years of data science, predictive-modeling, or advanced-analytics experience, including independent responsibility for complex analytical or scientific work.
- Demonstrated proficiency in analytical programming, advanced analytics and statistics, predictive modeling, machine learning, and data visualization.
- Strong proficiency in Python and/or R and experience working with large relational databases using SQL.
- Demonstrated experience developing and validating models or independently reviewing and validating models developed by others. Either experience route must demonstrate hands-on ability to implement analytical methods, test results, and critically assess scientific evidence.
- Deep knowledge of statistical reasoning, linear and other regression techniques, random forests, supervised and unsupervised learning, feature engineering, forecasting, model evaluation, and the selection and validation of data science methods.
- Ability to independently assess ambiguous problems, identify methodological limitations, recommend corrections, and communicate scientific and technical findings clearly.
- Ability to work effectively with clinical, operational, engineering, and external-partner teams; provide constructive technical feedback; manage multiple priorities; and support implementation of reproducible solutions.
PREFERRED QUALIFICATIONS
- M.A./M.S. in a related quantitative field.
- Experience with healthcare/EHR data, including Epic Clarity or Caboodle, and clinical-quality or operational measures.
- Experience with outcomes research, clinical research, population health, causal inference, optimization, or evaluation of interventions and workflows.
- Experience developing or evaluating natural-language processing, generative AI, or large language model solutions.
- Experience with cloud-based development, containerized solutions, computational infrastructure, modern data platforms, and data visualization.
- Knowledge of healthcare regulatory and reimbursement environments.
COMPENSATION
- $114,857.60 - $162,177.60 Annual
See all 249+ OPT Data Scientist Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship as a Data Scientist
Lead with your STEM OPT eligibility
Mention your STEM OPT authorization upfront in your resume summary and cover letter. Employers who understand the 24-month extension are far more likely to engage than those assuming standard 12-month OPT timelines.
Target companies with active USCIS filing histories
Companies that regularly file H-1B visa petitions are experienced with work authorization requirements. Focus your applications on employers with a documented track record of sponsoring international data professionals, not just those with open roles.
Quantify your technical impact, not just your tools
Listing Python and SQL is table stakes. Describe the outcome: a model that reduced churn by 15%, or a pipeline that cut processing time in half. Measurable results make sponsorship conversations easier for hiring managers to justify.
Apply before your OPT clock creates urgency
Start applying at least three to four months before your current authorization expires. Employers notice tight timelines and may hesitate. Earlier outreach signals that you're organized and gives both sides room to negotiate terms.
Clarify your authorization status in your first email
When reaching out to recruiters, briefly state your OPT status and STEM extension eligibility in one sentence. Removing ambiguity early filters out employers who won't sponsor and saves you time with those who will.
Build a portfolio that removes doubt about your skills
A GitHub repository with real data projects, documented methodology, and clean code answers the question before it's asked. Recruiters for Data Scientist roles regularly check portfolios before scheduling interviews.
Data Scientist OPT: Frequently Asked Questions
Does a Data Scientist role qualify for STEM OPT extension?
Yes, Data Scientist positions almost always qualify for the 24-month STEM OPT extension. The role falls under CIP codes tied to computer science, statistics, and applied mathematics. Your degree must be in a STEM-designated field and your employer must enroll in E-Verify. Confirm your degree's CIP code with your DSO before applying.
How do I find Data Scientist jobs that sponsor OPT students?
Migrate Mate filters job listings specifically for employers willing to sponsor international candidates, including OPT students. Searching there saves significant time compared to applying broadly and discovering sponsorship limitations late in the process. Focus on companies in tech, finance, healthcare, and consulting, which have the highest concentration of sponsored Data Scientist roles.
Can I work as a Data Scientist on OPT before my employer files for H-1B?
Yes. OPT authorizes full-time employment in a role directly related to your degree field. You don't need an H-1B to start working. If your employer files an H-1B cap-subject petition before April 1 and you maintain valid OPT, the cap-gap rule protects your authorization through September 30 while the petition is pending.
What degree fields support OPT authorization for Data Scientist roles?
Computer Science, Statistics, Mathematics, Data Science, Information Systems, and Engineering degrees are the most common qualifying fields. Applied economics and quantitative social science degrees may also qualify depending on how your school's DSO classified the program. The key requirement is that your job duties must be directly related to your degree field.
Do I need to report a Data Scientist job to my DSO while on OPT?
Yes. Within 10 days of starting employment, you must report your employer's name, address, job title, and start date to your DSO, who updates your SEVIS record. Failing to report can jeopardize your OPT status. If you change employers or take on a second position, each change requires a separate update.