OPT Data Engineering Intern Jobs
Data Engineering Intern roles are among the most OPT-friendly positions in tech: employers routinely file H-1B visa petitions for strong interns, and the work squarely qualifies as STEM OPT, giving you up to 36 months of authorized work. Most roles involve Python, SQL, and cloud pipelines, aligning directly with CS and data science degrees.
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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 1,422+ OPT Data Engineering Intern Jobs
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Get Access To All JobsTips for Finding OPT Sponsorship as a Data Engineering Intern
Lead with STEM OPT eligibility upfront
Mention your STEM OPT status in your resume summary or cover letter. Employers unfamiliar with OPT often assume sponsorship is expensive and complex. Clarifying that you're already authorized to work removes that friction before the first conversation.
Target companies with active H-1B filing history
Employers who have previously sponsored H-1B visas are far more likely to extend interns into full-time sponsored roles. Focusing your search on companies with that track record significantly improves your odds of long-term visa support after the internship ends.
Align your degree field to the job description explicitly
STEM OPT requires your employment to be directly related to your degree. In applications, name the connection clearly: a computer science degree supporting a role building data pipelines is obvious to you but worth spelling out for a hiring manager unfamiliar with OPT rules.
Treat your DSO relationship as a job search asset
Your Designated School Official can confirm your OPT authorization period and STEM extension eligibility in writing. Having that documentation ready before interviews signals to employers that you've handled the administrative side and won't create delays in onboarding.
Apply early relative to your OPT start date
Many employers won't hold an offer open for more than a few weeks. Start your internship search three to four months before your intended start date so your OPT authorization is active or imminent by the time an offer arrives, not still weeks away.
Negotiate a full-time conversion discussion before accepting
Internships that convert to full-time roles are where OPT students gain the most immigration leverage. Before accepting an offer, ask directly whether the company has a conversion process and whether it includes visa sponsorship support for international hires.
Data Engineering Intern OPT: Frequently Asked Questions
Can I work as a Data Engineering Intern on OPT?
Yes, provided the internship is directly related to your degree field. A data engineering role requires skills in areas like database design, pipeline development, and distributed systems, which align closely with degrees in computer science, data science, and related engineering disciplines. Your DSO can confirm whether the specific role qualifies before you accept an offer.
Does a Data Engineering Internship qualify for the STEM OPT extension?
It does if your degree is on the STEM Designated Degree Program List and the employer meets E-Verify enrollment requirements. Most data engineering internships at technology companies, financial institutions, and large enterprises satisfy both conditions. The extension gives you an additional 24 months of work authorization beyond your initial 12-month OPT period, totaling 36 months.
How do I find Data Engineering Intern roles that are open to OPT students?
Migrate Mate is built specifically for F-1 OPT students and surfaces data engineering internships at employers who are open to or actively supportive of international work authorization. Filtering by OPT-friendly employers saves significant time compared to applying broadly and discovering late in the process that a company won't work with international candidates.
Do employers need to do anything special to hire me as a Data Engineering Intern on OPT?
For standard OPT, no special filing is required from the employer. You present your EAD card and your employer verifies it through Form I-9 like any other employment authorization document. If you later apply for a STEM OPT extension, your employer must be enrolled in E-Verify and sign a formal training plan using Form I-983, which outlines your learning goals and supervision structure.
What happens to my OPT if my Data Engineering Internship ends early or the company rescinds the offer?
Your OPT authorization remains valid, but unemployment time counts against your 90-day limit for pre-completion OPT or 150-day limit for post-completion OPT. You must report any change in employer or employment status to your DSO within 10 days. If you're between roles, use that window actively to line up your next position before hitting the unemployment threshold.