OPT Data Manager Jobs
Data Manager jobs on OPT are widely available at companies that regularly file H-1B visa petitions for data roles. Most positions require a degree in information systems, computer science, or a related field, and STEM OPT extensions of up to 24 months apply when the role falls under a qualifying CIP code.
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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
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Get Access To All JobsTips for Finding OPT Sponsorship as a Data Manager
Target STEM-designated programs
Confirm your degree program has a STEM CIP code before applying. Data Manager roles typically qualify under Computer and Information Sciences, which supports the 24-month STEM OPT extension and makes you a stronger candidate for employers thinking long-term.
Focus on companies with H-1B filing history
Prioritize employers who have sponsored data professionals on H-1B visas before. Past sponsorship behavior is the strongest signal that a company has the legal infrastructure and willingness to support your post-OPT transition when the time comes.
Show technical and compliance overlap
Data Managers who demonstrate familiarity with data governance frameworks, GDPR, or HIPAA compliance stand out. Regulated industries like healthcare and finance hire heavily in this role and tend to have dedicated immigration counsel already in place.
Quantify the data infrastructure you have managed
Be specific about the scale of data you have managed, databases you have administered, and teams you have coordinated. Concrete metrics make it easier for hiring managers to justify sponsorship by demonstrating clear, measurable business value you bring.
Address OPT status directly and early
Raise your work authorization status before the final interview stage. Explain that you are authorized to work now and clarify the timeline. Employers who hesitate often do so because they misunderstand the process, not because they are unwilling.
Build relationships inside data teams
Networking with data engineers, analysts, and IT directors increases your chances of internal referrals. Employees who vouch for candidates often help move sponsorship conversations forward by reducing perceived hiring risk for the recruiting team.
Data Manager OPT: Frequently Asked Questions
Can I work as a Data Manager on OPT without additional authorization?
Yes. If your degree is in a qualifying field such as computer science, information systems, or data science, you can work as a Data Manager during your standard 12-month OPT period. If your program carries a STEM designation, you may also be eligible for a 24-month STEM OPT extension, giving you up to 36 months of authorized work in total.
Does a Data Manager role qualify for the STEM OPT extension?
It depends on your degree's CIP code, not the job title itself. If your degree falls under a STEM-designated CIP code, such as Computer and Information Sciences or Management Information Systems, and the Data Manager role is directly related to that field of study, it qualifies. Your DSO confirms eligibility based on your specific degree program.
How do I find Data Manager jobs that sponsor OPT and H-1B?
Migrate Mate filters job listings specifically for OPT-friendly employers who have a history of sponsoring work visas. Searching there saves significant time compared to applying broadly and discovering late in the process that a company does not sponsor. Look for employers in healthcare, finance, and enterprise technology, where data management roles are common and sponsorship infrastructure is already in place.
What happens to my OPT authorization if I switch from a Data Manager role to a different position?
Your OPT authorization is tied to employment that is directly related to your degree, not to a specific job title. Switching to a different role is permitted as long as the new position still relates to your field of study. You must report the change to your DSO within 10 days and update your record in the Student and Exchange Visitor Information System.
Do employers typically require a specific degree for Data Manager roles on OPT?
Most Data Manager positions require at least a bachelor's degree in computer science, information systems, data science, or a related technical field. Some roles, particularly in regulated industries, prefer a master's degree. Your degree must have a clear connection to the job for it to count as qualifying OPT employment under USCIS guidelines.