Applied Scientist Jobs in USA with Visa Sponsorship
Applied Scientists combine advanced research with practical implementation, making them strong candidates for H-1B visa and O-1 visa sponsorship. The role typically requires a PhD in computer science, machine learning, statistics, or related field, which easily satisfies specialty occupation requirements. Major tech companies regularly sponsor Applied Scientists, with approval rates generally high due to clear degree-to-role alignment.
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Are you passionate about using data science to transform how businesses understand and optimize customer interactions at scale? Do you want to build the models and analytics that power the next generation of AI-driven customer experiences while working directly with customers to accelerate production deployments?
As a Senior Applied Scientist within the Applied AI Solutions team, you will collaborate across AI Velocity Teams (AIVT), enabling multiple customer engagements simultaneously. You will lead data science initiatives that span the full lifecycle — from identifying high-value business problems and formulating hypotheses, through rigorous experimentation and modeling, to deploying production-grade solutions that serve thousands of customers. You will bring deep expertise in statistical inference, machine learning, and experimental design to drive measurable impact across Amazon Connect's analytics products and broader Connect AI initiatives.
A critical dimension of this role is working directly with customers during production pilots to accelerate time-to-value. You will partner with Applied AI Solutions Architects and Customer Success Specialists to design, build, and deploy AI solutions in customer environments during fixed deployment cycles. You will enable field teams with data-driven insights, reusable analytical assets, ROI tools, and scalable tooling that accelerate customer engagements and solution delivery. Your work will directly influence customer decisions to adopt Connect Customer AI by quantifying business outcomes and demonstrating measurable value.
You will operate with significant autonomy, owning the scientific direction of your projects while collaborating with applied scientists, software engineers, product managers, technical, and business stakeholders. You will be expected to identify the right methodology for each problem — whether that's a classical statistical approach, a modern deep learning technique, or a novel combination — and communicate your findings clearly to both technical and non-technical audiences. This role spans Connect AI initiatives including conversational analytics and agentic AI capabilities, offering the opportunity to pioneer data science approaches that scale intelligent analytics worldwide.
Key job responsibilities
- Design, develop, and deploy statistical models and machine learning pipelines to drive product improvements and business decisions
- Work directly with customers during production pilots to design, build, and deploy AI solutions that demonstrate measurable business value
- Design and execute A/B experiments and causal inference analyses to measure the impact of new features and model changes on customer outcomes
- Build ROI models and business case tools that quantify the value of Connect Customer AI for existing customers transitioning from Connect Customer Basic
- Develop and maintain forecasting systems for demand prediction, capacity planning, and workforce optimization
- Develop and apply NLP and generative AI techniques to extract insights from structured and unstructured data at scale
- Partner with applied scientists and software engineers to productionize models, ensuring reliability, monitoring, and operational excellence
- Enable AI Velocity teams with reusable analytical assets, diagnostic notebooks, and scalable tooling that accelerate customer engagements
- Build benchmarking studies and optimization frameworks that demonstrate value across customer cohorts
- Own success metrics and create mechanisms to measure model performance, adoption, and business impact
- Communicate findings and technical trade-offs to senior leadership and customer executives through written documents (6-pagers, science reviews) and presentations
- Operate as a shared resource across 2-3 AIVT teams simultaneously, providing data science expertise across multiple customer engagements
A day in the life
- Start the morning on a call with the AI Velocity Teams preparing for a strategic customer engagement — reviewing the analytical assets and dashboards you've built, walking through how to interpret model outputs, and tailoring recommendations to the customer's contact center environment
- Join a customer working session where you're deploying a production pilot — analyzing their historical contact data, building demand forecasting models, and demonstrating how AI optimizations will reduce their cost per serviced contact while improving customer experience metrics
- Dive into a deep analysis triggered by AIVT field feedback — a large enterprise customer is seeing unexpected patterns in their contact data, and you're pulling together multi-source data to isolate root cause and build a reusable diagnostic notebook the AIVT team can leverage for similar cases
- Participate in a Conversational Analytics science review, presenting your A/B test results on a new sentiment classification approach and discussing trade-offs between model accuracy and inference latency with the engineering team
- Spend the afternoon building a reusable ROI calculator that field teams can use across customer engagements — packaging your economic models with configurable parameters so teams can quickly quantify the value of Connect Customer AI for different customer profiles and usage patterns
- Collaborate with AI Architects and Customer Success Specialists across your three active AIVT engagements, providing data science guidance on model selection, evaluation frameworks, and success metrics for each customer's unique use cases
- Wrap up by reviewing a design document for an agentic AI feature that will use conversation analytics to automatically surface coaching recommendations for contact center supervisors, providing feedback on the evaluation methodology and success metrics
About the team
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
BASIC QUALIFICATIONS
- Master's degree in engineering, statistics, computer science, mathematics, or a related quantitative field
- 5+ years of quantitative and qualitative data science/business intelligence with significant business impact experience
- 3+ years of machine learning, statistical modeling, data mining, and analytics techniques experience
- PhD, or PhD and 4+ years of designing experiments and statistical analysis of results experience
- Experience in A/B testing
- Proficiency in Python and SQL; experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost
- Track record of delivering end-to-end data science solutions from problem definition through production deployment
PREFERRED QUALIFICATIONS
- PhD in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- Experience with AI/ML technologies
- Knowledge of AWS platforms such as S3, Glue, Athena, Sagemaker
- Experience working directly in customer implementations
- Experience building and managing financial models for business forecasting and problem solving, or experience in Excel (macros, index, conditional list, arrays, pivots, lookups)
- Experience building MLOps workflows (CI/CD for models, feature stores, model registries) or real-time inference systems
- Publications at peer-reviewed conferences or journals (NeurIPS, ICML, KDD, ACL, EMNLP, etc.)
- Experience with contact center, customer experience, or telecommunications data
- Proven ability to influence without authority and communicate effectively across organizational boundaries
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
LOCATION
USA, WA, Seattle
COMPENSATION
- Salary Range: $167,100.00 - $226,100.00 USD annually
DESCRIPTION
Are you passionate about using data science to transform how businesses understand and optimize customer interactions at scale? Do you want to build the models and analytics that power the next generation of AI-driven customer experiences while working directly with customers to accelerate production deployments?
As a Senior Applied Scientist within the Applied AI Solutions team, you will collaborate across AI Velocity Teams (AIVT), enabling multiple customer engagements simultaneously. You will lead data science initiatives that span the full lifecycle — from identifying high-value business problems and formulating hypotheses, through rigorous experimentation and modeling, to deploying production-grade solutions that serve thousands of customers. You will bring deep expertise in statistical inference, machine learning, and experimental design to drive measurable impact across Amazon Connect's analytics products and broader Connect AI initiatives.
A critical dimension of this role is working directly with customers during production pilots to accelerate time-to-value. You will partner with Applied AI Solutions Architects and Customer Success Specialists to design, build, and deploy AI solutions in customer environments during fixed deployment cycles. You will enable field teams with data-driven insights, reusable analytical assets, ROI tools, and scalable tooling that accelerate customer engagements and solution delivery. Your work will directly influence customer decisions to adopt Connect Customer AI by quantifying business outcomes and demonstrating measurable value.
You will operate with significant autonomy, owning the scientific direction of your projects while collaborating with applied scientists, software engineers, product managers, technical, and business stakeholders. You will be expected to identify the right methodology for each problem — whether that's a classical statistical approach, a modern deep learning technique, or a novel combination — and communicate your findings clearly to both technical and non-technical audiences. This role spans Connect AI initiatives including conversational analytics and agentic AI capabilities, offering the opportunity to pioneer data science approaches that scale intelligent analytics worldwide.
Key job responsibilities
- Design, develop, and deploy statistical models and machine learning pipelines to drive product improvements and business decisions
- Work directly with customers during production pilots to design, build, and deploy AI solutions that demonstrate measurable business value
- Design and execute A/B experiments and causal inference analyses to measure the impact of new features and model changes on customer outcomes
- Build ROI models and business case tools that quantify the value of Connect Customer AI for existing customers transitioning from Connect Customer Basic
- Develop and maintain forecasting systems for demand prediction, capacity planning, and workforce optimization
- Develop and apply NLP and generative AI techniques to extract insights from structured and unstructured data at scale
- Partner with applied scientists and software engineers to productionize models, ensuring reliability, monitoring, and operational excellence
- Enable AI Velocity teams with reusable analytical assets, diagnostic notebooks, and scalable tooling that accelerate customer engagements
- Build benchmarking studies and optimization frameworks that demonstrate value across customer cohorts
- Own success metrics and create mechanisms to measure model performance, adoption, and business impact
- Communicate findings and technical trade-offs to senior leadership and customer executives through written documents (6-pagers, science reviews) and presentations
- Operate as a shared resource across 2-3 AIVT teams simultaneously, providing data science expertise across multiple customer engagements
A day in the life
- Start the morning on a call with the AI Velocity Teams preparing for a strategic customer engagement — reviewing the analytical assets and dashboards you've built, walking through how to interpret model outputs, and tailoring recommendations to the customer's contact center environment
- Join a customer working session where you're deploying a production pilot — analyzing their historical contact data, building demand forecasting models, and demonstrating how AI optimizations will reduce their cost per serviced contact while improving customer experience metrics
- Dive into a deep analysis triggered by AIVT field feedback — a large enterprise customer is seeing unexpected patterns in their contact data, and you're pulling together multi-source data to isolate root cause and build a reusable diagnostic notebook the AIVT team can leverage for similar cases
- Participate in a Conversational Analytics science review, presenting your A/B test results on a new sentiment classification approach and discussing trade-offs between model accuracy and inference latency with the engineering team
- Spend the afternoon building a reusable ROI calculator that field teams can use across customer engagements — packaging your economic models with configurable parameters so teams can quickly quantify the value of Connect Customer AI for different customer profiles and usage patterns
- Collaborate with AI Architects and Customer Success Specialists across your three active AIVT engagements, providing data science guidance on model selection, evaluation frameworks, and success metrics for each customer's unique use cases
- Wrap up by reviewing a design document for an agentic AI feature that will use conversation analytics to automatically surface coaching recommendations for contact center supervisors, providing feedback on the evaluation methodology and success metrics
About the team
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
BASIC QUALIFICATIONS
- Master's degree in engineering, statistics, computer science, mathematics, or a related quantitative field
- 5+ years of quantitative and qualitative data science/business intelligence with significant business impact experience
- 3+ years of machine learning, statistical modeling, data mining, and analytics techniques experience
- PhD, or PhD and 4+ years of designing experiments and statistical analysis of results experience
- Experience in A/B testing
- Proficiency in Python and SQL; experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost
- Track record of delivering end-to-end data science solutions from problem definition through production deployment
PREFERRED QUALIFICATIONS
- PhD in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- Experience with AI/ML technologies
- Knowledge of AWS platforms such as S3, Glue, Athena, Sagemaker
- Experience working directly in customer implementations
- Experience building and managing financial models for business forecasting and problem solving, or experience in Excel (macros, index, conditional list, arrays, pivots, lookups)
- Experience building MLOps workflows (CI/CD for models, feature stores, model registries) or real-time inference systems
- Publications at peer-reviewed conferences or journals (NeurIPS, ICML, KDD, ACL, EMNLP, etc.)
- Experience with contact center, customer experience, or telecommunications data
- Proven ability to influence without authority and communicate effectively across organizational boundaries
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
LOCATION
USA, WA, Seattle
COMPENSATION
- Salary Range: $167,100.00 - $226,100.00 USD annually
See all 326+ Applied Scientist jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Applied Scientist roles.
Get Access To All JobsTips for Finding Visa Sponsorship as an Applied Scientist
Emphasize your PhD and research publications
Applied Scientist roles strongly favor PhD holders. Highlight your dissertation research, peer-reviewed publications, and conference presentations to demonstrate the advanced knowledge that justifies H-1B specialty occupation requirements.
Target companies with established ML research divisions
Amazon, Google, Microsoft, and Meta regularly sponsor Applied Scientists. These companies understand the role's requirements and have streamlined visa processes for research-focused positions with clear business applications.
Document your track record of research-to-product translation
Applied Scientists bridge research and implementation. Showcase projects where your research led to deployed systems, improved algorithms, or measurable business impact to strengthen your visa petition.
Prepare for technical interviews that demonstrate specialized knowledge
Visa interviews may include questions about your research methodology and how it applies to the role. Be ready to explain complex algorithms and statistical methods in accessible terms.
Highlight interdisciplinary expertise in your petition
Applied Scientists often work across domains like computer vision, NLP, or robotics. Document how your cross-disciplinary knowledge creates unique value that requires specialized H-1B talent rather than general programmers.
Applied Scientist jobs are hiring across the US. Find yours.
Find Applied Scientist JobsFrequently Asked Questions
What degree do I need for Applied Scientist H-1B sponsorship?
Most Applied Scientist positions require a PhD in computer science, machine learning, statistics, mathematics, or closely related field. Some roles accept a Master's with extensive research experience, but PhD holders have significantly stronger H-1B petitions due to the advanced specialization required for research-to-product development.
Which companies sponsor Applied Scientists for visas?
Major tech companies including Amazon, Google, Microsoft, Meta, Apple, and Netflix regularly sponsor Applied Scientists. Research-focused startups and companies in autonomous vehicles, healthcare AI, and fintech also sponsor, though they may have less experience with the visa process than established tech giants.
How do Applied Scientist H-1B approval rates compare to other roles?
Applied Scientist positions generally have high H-1B approval rates due to clear specialty occupation requirements. The PhD requirement and specialized research knowledge make it easy to demonstrate that the role requires theoretical and practical application of highly specialized knowledge in computer science or mathematics.
How to find Applied Scientist jobs with visa sponsorship?
To find Applied Scientist positions with visa sponsorship, use Migrate Mate, which specializes in connecting international talent with sponsoring employers. Focus on tech companies, research institutions, and AI startups that commonly hire Applied Scientists on H-1B, O-1, or EB-2 visas. These roles are in high demand at companies developing machine learning, data science, and research products.
Can I get sponsored as an Applied Scientist with only industry experience?
While possible, it's challenging without advanced degrees. USCIS typically expects Applied Scientists to have PhD-level research training. If you only have industry experience, you'd need to demonstrate extraordinary depth in machine learning research, publications, or patents that substitute for formal academic credentials.
Should I apply for O-1 instead of H-1B as an Applied Scientist?
Consider O-1 if you have exceptional research credentials like high-impact publications, significant citations, conference awards, or recognition as a leading researcher in AI/ML. O-1 has no lottery, faster processing, and allows more flexibility, but requires demonstrating extraordinary ability rather than just specialty occupation requirements.
What is the prevailing wage requirement for sponsored Applied Scientist jobs?
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.
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