Data Science Engineer Visa Sponsorship Jobs in New York
New York is one of the most active states for data science engineer visa sponsorship, driven by major employers in finance, tech, and media. Companies like JPMorgan Chase, Bloomberg, Amazon, and IBM regularly hire and sponsor data science engineers across New York City, with additional opportunities in Buffalo and Albany's growing tech corridors.
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Get Access To All JobsJob Type: Officer of Administration
Bargaining Unit:
Regular/Temporary: Regular
End Date if Temporary:
Hours Per Week: 35
Standard Work Schedule: Monday - Friday
Building: PH-20
Salary Range: $160,000 - $180,000
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.
Position Summary
The Department of Biomedical Informatics at Columbia University is seeking a highly motivated data science engineer to support large-scale observational research within the OHDSI (Observational Health Data Sciences and Informatics) network. This role will focus on the design, implementation, and execution of distributed network studies using electronic health record (EHR) and administrative claims data to generate real-world evidence.
The successful candidate will contribute to characterization, population-level estimation (causal inference), and patient-level prediction analyses across multi-institutional data networks. This position offers a unique opportunity to work at the intersection of biomedical informatics, data science, and clinical research within a leading academic medical center.
This position is a full-time two-year position with a possibility of an extension, contingent on available funding.
Responsibilities
Key Responsibilities
- Design and implement observational network studies using distributed EHR and administrative claims data
- Conduct large-scale characterization, comparative effectiveness and safety estimation, and patient-level prediction analyses
- Develop reproducible analytic pipelines using R and SQL in relational database environments
- Apply and evaluate methods from causal inference (e.g., confounding control, bias assessment, sensitivity analyses)
- Apply machine learning approaches for predictive modeling using high-dimensional healthcare data
- Work with standardized data representations, including the OMOP Common Data Model and standardized clinical vocabularies for conditions, drugs, procedures, and measurements
- Collaborate with interdisciplinary teams including clinicians, statisticians, data engineers, and informaticians
- Contribute to scholarly outputs including manuscripts, presentations, and open-source analytic tools
- Support transparent, reproducible, and scalable research practices across distributed data networks
Minimum Qualifications
Master’s degree in biostatistics, public health, epidemiology, informatics, computer science, or related field, and or equivalent in education and experience, with at least 2 years’ related experience.
- At least 1 year of relevant prior work experience in the healthcare industry within a health system, a pharmaceutical company, or an insurer
- Strong programming experience in R and SQL
- Experience working with relational databases and large-scale healthcare datasets
- Demonstrated interest in observational research using real-world clinical or claims data
- Ability to design, implement, and document reproducible analytic workflows
- Strong written and verbal communication skills
Preferred Qualifications
PhD degree in biostatistics, public health, epidemiology, informatics, computer science, or related field, and/or equivalent and experience in education, with at least 1 year of related work experience.
Other Requirements
- Familiarity with the OMOP Common Data Model and standardized vocabularies (e.g., ICD, NDC, SNOMED, MedDRA, LOINC, CPT)
- Knowledge of causal inference methods for observational studies
- Experience with machine learning techniques for patient-level prediction
- Prior experience working in distributed or federated data networks
- Familiarity with open-source research ecosystems and collaborative scientific communities
Work Environment & Opportunities
This position is based at Columbia University’s Department of Biomedical Informatics, a world leader in clinical research informatics and observational health data science. The role offers close collaboration with leading researchers, access to large-scale real-world data, and opportunities to contribute to impactful, open, and methodologically rigorous research that informs clinical and policy decision-making. This position is based in New York, NY and has the option to follow a hybrid schedule of 3 days per week working on site and 2 days per week working remotely.
Equal Opportunity Employer / Disability / Veteran
Columbia University is committed to the hiring of qualified local residents.
Job Type: Officer of Administration
Bargaining Unit:
Regular/Temporary: Regular
End Date if Temporary:
Hours Per Week: 35
Standard Work Schedule: Monday - Friday
Building: PH-20
Salary Range: $160,000 - $180,000
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.
Position Summary
The Department of Biomedical Informatics at Columbia University is seeking a highly motivated data science engineer to support large-scale observational research within the OHDSI (Observational Health Data Sciences and Informatics) network. This role will focus on the design, implementation, and execution of distributed network studies using electronic health record (EHR) and administrative claims data to generate real-world evidence.
The successful candidate will contribute to characterization, population-level estimation (causal inference), and patient-level prediction analyses across multi-institutional data networks. This position offers a unique opportunity to work at the intersection of biomedical informatics, data science, and clinical research within a leading academic medical center.
This position is a full-time two-year position with a possibility of an extension, contingent on available funding.
Responsibilities
Key Responsibilities
- Design and implement observational network studies using distributed EHR and administrative claims data
- Conduct large-scale characterization, comparative effectiveness and safety estimation, and patient-level prediction analyses
- Develop reproducible analytic pipelines using R and SQL in relational database environments
- Apply and evaluate methods from causal inference (e.g., confounding control, bias assessment, sensitivity analyses)
- Apply machine learning approaches for predictive modeling using high-dimensional healthcare data
- Work with standardized data representations, including the OMOP Common Data Model and standardized clinical vocabularies for conditions, drugs, procedures, and measurements
- Collaborate with interdisciplinary teams including clinicians, statisticians, data engineers, and informaticians
- Contribute to scholarly outputs including manuscripts, presentations, and open-source analytic tools
- Support transparent, reproducible, and scalable research practices across distributed data networks
Minimum Qualifications
Master’s degree in biostatistics, public health, epidemiology, informatics, computer science, or related field, and or equivalent in education and experience, with at least 2 years’ related experience.
- At least 1 year of relevant prior work experience in the healthcare industry within a health system, a pharmaceutical company, or an insurer
- Strong programming experience in R and SQL
- Experience working with relational databases and large-scale healthcare datasets
- Demonstrated interest in observational research using real-world clinical or claims data
- Ability to design, implement, and document reproducible analytic workflows
- Strong written and verbal communication skills
Preferred Qualifications
PhD degree in biostatistics, public health, epidemiology, informatics, computer science, or related field, and/or equivalent and experience in education, with at least 1 year of related work experience.
Other Requirements
- Familiarity with the OMOP Common Data Model and standardized vocabularies (e.g., ICD, NDC, SNOMED, MedDRA, LOINC, CPT)
- Knowledge of causal inference methods for observational studies
- Experience with machine learning techniques for patient-level prediction
- Prior experience working in distributed or federated data networks
- Familiarity with open-source research ecosystems and collaborative scientific communities
Work Environment & Opportunities
This position is based at Columbia University’s Department of Biomedical Informatics, a world leader in clinical research informatics and observational health data science. The role offers close collaboration with leading researchers, access to large-scale real-world data, and opportunities to contribute to impactful, open, and methodologically rigorous research that informs clinical and policy decision-making. This position is based in New York, NY and has the option to follow a hybrid schedule of 3 days per week working on site and 2 days per week working remotely.
Equal Opportunity Employer / Disability / Veteran
Columbia University is committed to the hiring of qualified local residents.
Data Science Engineer Job Roles in New York
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Search Data Science Engineer Jobs in New YorkData Science Engineer Jobs in New York: Frequently Asked Questions
Which companies sponsor visas for data science engineers in New York?
Financial institutions and tech firms lead sponsorship activity for data science engineers in New York. JPMorgan Chase, Citigroup, Goldman Sachs, and Bloomberg are consistent sponsors in the finance sector, while Amazon, IBM, Google, and Meta maintain large data science teams in New York City. Healthcare organizations like Memorial Sloan Kettering and Mount Sinai also sponsor data science engineering roles.
Which visa types are most common for data science engineer roles in New York?
The H-1B visa is the most common visa for data science engineers in New York, as the role typically qualifies as a specialty occupation requiring at least a bachelor's degree in a related technical field such as computer science, statistics, or data engineering. Some employers also sponsor EB-2 or EB-3 green card petitions. Candidates from Australia may qualify for the E-3 visa, and Canadian and Mexican nationals may be eligible for the TN visa.
How to find data science engineer visa sponsorship jobs in New York?
Migrate Mate is built specifically for international job seekers and filters data science engineer roles in New York by visa sponsorship history, so you're not wasting time on employers unlikely to sponsor. Search by role and state to surface companies that have filed H-1B or other work visa petitions for data science positions in New York, including both large financial institutions and mid-size tech firms.
Which cities in New York have the most data science engineer sponsorship jobs?
New York City accounts for the overwhelming majority of data science engineer sponsorship jobs in the state, concentrated in Midtown and Lower Manhattan where financial services firms, media companies, and tech offices cluster. Brooklyn's tech community in DUMBO and Industry City adds additional opportunities. Buffalo has seen growth from companies like M&T Bank and local university research partnerships, though its volume remains significantly smaller than NYC.
Are there any state-specific factors that affect visa sponsorship for data science engineers in New York?
New York's concentration of financial services employers means data science engineers here often work on regulated data pipelines, risk models, and compliance systems, which can make employer sponsorship decisions more deliberate given legal and regulatory oversight. New York's universities, including Columbia, Cornell Tech, and NYU, produce a strong local talent pipeline, and many employers recruit directly from these programs, sometimes extending sponsorship to international OPT students already in the state.
What is the prevailing wage for sponsored data science engineer jobs in New York?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.