Staff Scientist Visa Sponsorship Jobs in New York
Staff scientist visa sponsorship jobs in New York are concentrated in the state's dense network of research institutions, pharmaceutical companies, and biotech firms. Major employers include Memorial Sloan Kettering, Pfizer, Bristol Myers Squibb, and Cold Spring Harbor Laboratory. New York City, the Hudson Valley, and Long Island's biopharma corridor are the primary hiring centers for sponsored staff scientist positions.
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The Mailchimp Product Data Science team turns product and customer data into insights and models that shape Mailchimp's product strategy. We work as thought partners with Product, Design, Engineering, Marketing, Finance, and Pricing to drive data-driven decisions through experimentation, causal measurement, and deep customer analytics.
We're looking for a Senior Staff Data Scientist to be an analytical leader for Mailchimp's product growth. In this high-impact individual contributor role, you'll set the measurement vision across the full customer journey: sign-up and onboarding, activation, conversion to paid, engagement, and long-term retention. The role raises the scientific bar for how we experiment and size opportunities, and it shapes product and business strategy at the senior leadership level. It's also hands-on, focused on the highest-leverage problems and on helping the team adopt AI-native workflows that let us move faster.
This role is based in New York, NY / Mountain View, CA / Atlanta, GA under Intuit's hybrid model, 3 days per week in office.
Responsibilities
- Influence product strategy: Be a strategic thought partner to senior Product and business leaders. Shape Mailchimp's growth roadmap with deep dives into customer segmentation, onboarding and activation funnels, feature adoption, pricing and packaging, and retention.
- Set the analytics vision: Own the measurement framework, north-star and guardrail metrics, and leading indicators for the self-serve customer journey, from free and trial sign-ups to paid conversion, bookings, and recurring revenue.
- Lead experimentation and causal measurement: Drive a culture of rigorous, iterative experimentation. Design complex tests (A/B/n, sequential, holdouts, long-running), apply Bayesian and frequentist inference, and use causal inference methods such as difference-in-differences, propensity score matching, and synthetic control where randomized tests aren't possible.
- Size and prioritize opportunities: Build opportunity-sizing and forecasting frameworks that turn product initiatives into incremental revenue impact, accounting for retention curves and the winner's curse, so leadership can prioritize investment.
- Uncover growth opportunities: Use first-principles thinking and exploratory analysis to find untapped levers across the funnel, including in-product onboarding, lifecycle marketing, paid media, and plan upgrades.
- Shape the AI-native roadmap: Partner with Product and Engineering to measure AI-powered features in Mailchimp. Build ML and LLM-based solutions such as propensity models, personalization, and automated insights for hard product problems.
- Raise the scientific bar: Build reusable analytical frameworks, governed metric definitions, and self-serve tools that improve decision-making at scale. Champion strong instrumentation and data quality.
- Lead and mentor: Be a technical lead for the Product Data Science team. Coach and grow senior and staff data scientists, review their methods, and model executive-ready storytelling.
Qualifications
- 8+ years in data science, product analytics, or a related field, with a proven record of driving product strategy and measurable business impact.
- Experience in B2B SaaS, subscription, or freemium/self-serve businesses strongly preferred. Familiarity with marketing technology or small-business customers is a plus.
- Expert SQL and data modeling skills on cloud data warehouses (BigQuery preferred), plus advanced Python or R.
- Deep expertise in experimental design and causal inference, with the judgment to balance statistical rigor against speed of decision-making.
- Track record of sizing opportunities and forecasting revenue impact, including funnel, cohort, and retention analysis.
- Hands-on experience applying machine learning (classification, regression, uplift or propensity modeling) and LLM/GenAI workflows to real product problems.
- Proficient in an AI-native data science tech stack (for example, Claude, Cursor, or Codex with MCPs, CLIs, and APIs) and eager to keep building how the team works with AI.
- Excellent executive communication. You turn complex analysis into clear narratives and recommendations, and you've influenced senior leaders across Product, Marketing, and Finance.
- Experience leading ambiguous, cross-functional work where the question, metric definitions, or methodology have to be set before the analysis can start.
- Demonstrated mentorship and technical leadership of other data scientists.
- Bachelor's or graduate degree in a quantitative field such as Statistics, Economics, Computer Science, Mathematics, or Operations Research, or equivalent practical experience.
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
The expected base pay range for this position is:
New York $210,500 - $284,500
Overview
The Mailchimp Product Data Science team turns product and customer data into insights and models that shape Mailchimp's product strategy. We work as thought partners with Product, Design, Engineering, Marketing, Finance, and Pricing to drive data-driven decisions through experimentation, causal measurement, and deep customer analytics.
We're looking for a Senior Staff Data Scientist to be an analytical leader for Mailchimp's product growth. In this high-impact individual contributor role, you'll set the measurement vision across the full customer journey: sign-up and onboarding, activation, conversion to paid, engagement, and long-term retention. The role raises the scientific bar for how we experiment and size opportunities, and it shapes product and business strategy at the senior leadership level. It's also hands-on, focused on the highest-leverage problems and on helping the team adopt AI-native workflows that let us move faster.
This role is based in New York, NY / Mountain View, CA / Atlanta, GA under Intuit's hybrid model, 3 days per week in office.
Responsibilities
- Influence product strategy: Be a strategic thought partner to senior Product and business leaders. Shape Mailchimp's growth roadmap with deep dives into customer segmentation, onboarding and activation funnels, feature adoption, pricing and packaging, and retention.
- Set the analytics vision: Own the measurement framework, north-star and guardrail metrics, and leading indicators for the self-serve customer journey, from free and trial sign-ups to paid conversion, bookings, and recurring revenue.
- Lead experimentation and causal measurement: Drive a culture of rigorous, iterative experimentation. Design complex tests (A/B/n, sequential, holdouts, long-running), apply Bayesian and frequentist inference, and use causal inference methods such as difference-in-differences, propensity score matching, and synthetic control where randomized tests aren't possible.
- Size and prioritize opportunities: Build opportunity-sizing and forecasting frameworks that turn product initiatives into incremental revenue impact, accounting for retention curves and the winner's curse, so leadership can prioritize investment.
- Uncover growth opportunities: Use first-principles thinking and exploratory analysis to find untapped levers across the funnel, including in-product onboarding, lifecycle marketing, paid media, and plan upgrades.
- Shape the AI-native roadmap: Partner with Product and Engineering to measure AI-powered features in Mailchimp. Build ML and LLM-based solutions such as propensity models, personalization, and automated insights for hard product problems.
- Raise the scientific bar: Build reusable analytical frameworks, governed metric definitions, and self-serve tools that improve decision-making at scale. Champion strong instrumentation and data quality.
- Lead and mentor: Be a technical lead for the Product Data Science team. Coach and grow senior and staff data scientists, review their methods, and model executive-ready storytelling.
Qualifications
- 8+ years in data science, product analytics, or a related field, with a proven record of driving product strategy and measurable business impact.
- Experience in B2B SaaS, subscription, or freemium/self-serve businesses strongly preferred. Familiarity with marketing technology or small-business customers is a plus.
- Expert SQL and data modeling skills on cloud data warehouses (BigQuery preferred), plus advanced Python or R.
- Deep expertise in experimental design and causal inference, with the judgment to balance statistical rigor against speed of decision-making.
- Track record of sizing opportunities and forecasting revenue impact, including funnel, cohort, and retention analysis.
- Hands-on experience applying machine learning (classification, regression, uplift or propensity modeling) and LLM/GenAI workflows to real product problems.
- Proficient in an AI-native data science tech stack (for example, Claude, Cursor, or Codex with MCPs, CLIs, and APIs) and eager to keep building how the team works with AI.
- Excellent executive communication. You turn complex analysis into clear narratives and recommendations, and you've influenced senior leaders across Product, Marketing, and Finance.
- Experience leading ambiguous, cross-functional work where the question, metric definitions, or methodology have to be set before the analysis can start.
- Demonstrated mentorship and technical leadership of other data scientists.
- Bachelor's or graduate degree in a quantitative field such as Statistics, Economics, Computer Science, Mathematics, or Operations Research, or equivalent practical experience.
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
The expected base pay range for this position is:
New York $210,500 - $284,500
Staff Scientist Job Roles in New York
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Search Staff Scientist Jobs in New YorkStaff Scientist Jobs in New York: Frequently Asked Questions
Which companies sponsor visas for staff scientists in New York?
Major sponsors include Memorial Sloan Kettering Cancer Center, Cold Spring Harbor Laboratory, Pfizer, Bristol Myers Squibb, Regeneron Pharmaceuticals, and Rockefeller University. Academic medical centers such as Weill Cornell Medicine and NYU Langone Health also file regularly for staff scientists. These organizations have established immigration infrastructure and documented histories of sponsoring H-1B visa and employment-based green card petitions for research roles.
What visa types realistically fit staff scientist roles in New York?
Staff scientist positions qualify as specialty occupations requiring at least a bachelor's degree in a directly related scientific field, making H-1B the most common path. Employers file a Labor Condition Application and an I-129 petition with USCIS. EB-2 and EB-3 employment-based green cards are the typical permanent residence routes, usually requiring PERM labor certification. F-1 students may use OPT or STEM OPT if their degree is STEM-designated. Australians may apply via the E-3 visa.
Which cities in New York have the most staff scientist sponsorship jobs?
New York City holds the highest concentration, driven by its academic medical centers, hospital research divisions, and biotech startups in Manhattan and Brooklyn. Long Island is a secondary hub, anchored by Cold Spring Harbor Laboratory and the broader biopharma cluster along the Route 110 corridor. The Capital Region around Albany also sees sponsorship activity through state health research agencies and life sciences companies expanding upstate.
How to find staff scientist visa sponsorship jobs in New York?
Migrate Mate lists staff scientist roles at New York employers with verified visa sponsorship history, searchable by city and research discipline. Filtering to New York surfaces positions at institutions and companies that have actively filed for sponsored scientists, so you can focus your applications where sponsorship is already established rather than asking cold. This saves time compared to manually screening postings with no sponsorship information.
Are there state-specific factors that affect staff scientist visa sponsorship in New York?
New York's density of NIH-funded research institutions and its large pharmaceutical cluster means a higher baseline of employers with active H-1B filing programs. Many positions are tied to grant-funded projects, so hiring timelines can follow federal funding cycles rather than standard recruitment windows. Academic employers such as Columbia, Cornell, and Rockefeller also sponsor J-1 visa research scholar appointments, which carry a two-year home residency requirement under section 212(e) of the Immigration and Nationality Act.
What is the prevailing wage for sponsored staff scientist 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.