Senior Data Science Engineer Jobs
Senior Data Science Engineer jobs are open across technology, finance, healthcare, and e-commerce, from mid-level to staff and principal, with specializations in machine learning infrastructure, large-scale data pipelines, and applied modeling. Find a role that fits from the openings below and apply directly.
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ADP is Hiring a Manager Data Science - Fraud
Job Summary
The Manager, Fraud Data Science is responsible for the performance, optimization, governance, and strategic expansion of ADP's Fraud Prevention Platform (FPP) and its associated fraud controls. This role owns the platform's real-time decisioning capabilities, including rules, models, decision strategies, third-party risk signals, AI-driven fraud detection capabilities, and control frameworks, ensuring they remain aligned with evolving fraud threats, enterprise risk appetite, client experience, and business growth objectives.
FPP is a foundational component of ADP's fraud strategy, and this role will help rapidly mature the platform, broaden its use cases, and expand its capabilities across ADP. The Manager will partner closely with other leaders across Global Fraud Prevention, Global Security, business teams, and engineering to translate emerging threats, fraud intelligence, investigative insights, and operational feedback into scalable, production-ready fraud controls that address an ever-changing fraud risk landscape.
This role will help shape the future direction of fraud decisioning at ADP. Success requires deep fraud prevention expertise, strong analytical and technical aptitude, experience with modern fraud decisioning platforms such as Feedzai, and the ability to balance fraud risk, client experience, operational efficiency, and business growth while continuously advancing the capabilities and adoption of ADP's enterprise fraud platform.
Like what you see? Apply now!
Learn more about ADP at tech.adp.com/careers
Key Responsibilities
- Own the strategy, performance, resilience, governance, and continuous evolution of the Fraud Prevention Platform (FPP) and its associated fraud controls.
- Drive real-time fraud decisioning capabilities, including rules, models, AI-enabled detection capabilities, decision strategies, and third-party risk signals to address evolving fraud threats and business needs.
- Monitor and optimize platform effectiveness through defined performance metrics, including fraud prevented, fraud losses, detection rates, false positive rates, operational impact, transaction volumes, and decision latency.
- Establish model and decisioning performance, testing, and experimentation frameworks, including champion-challenger strategies, A/B testing, simulation, controlled rollouts, and post-implementation validation to measure effectiveness, improve business outcomes, and support risk-based decision making.
- Develop closed-loop performance improvement processes that leverage fraud outcomes, production monitoring, threat intelligence, investigative findings, and operational feedback to continuously enhance fraud controls, decision strategies, and model performance.
- Oversee platform health and resilience, including uptime, transaction processing, integrations, third-party data providers, and system performance. Establish processes to rapidly identify and resolve service disruptions, data issues, and degraded control performance.
- Establish and maintain governance and change-management processes covering rule and model deployment, testing, approvals, documentation, monitoring, rollback, and ongoing performance management.
- Evaluate, optimize, and govern third-party fraud, identity, device intelligence, behavioral, consortium, and other risk data sources to maximize decision quality, fraud detection effectiveness, and return on investment.
- Define and execute the roadmap to mature FPP and expand fraud decisioning capabilities across products, channels, client segments, and enterprise use cases, including the adoption of advanced analytics and AI-driven capabilities.
- Partner within the Global Fraud Prevention group as well as with Global Security, Engineering, and business stakeholders to translate fraud risks and requirements into scalable platform capabilities and measurable business outcomes.
- Serve as the enterprise subject matter expert for FPP, advising stakeholders on platform capabilities, decisioning strategies, AI/ML-enabled functionality, integrations, data requirements, and fraud control effectiveness.
To Succeed in This role
- You'll have a bachelor’s degree or equivalent practical experience in risk management, fraud analytics, data science, computer science, statistics, engineering, or a related field.
Required Qualifications
- 5-7+ years of experience in fraud prevention, fraud detection, risk decisioning, payments risk, identity risk, financial crime, or related discipline.
- Experience with Python is required.
- Experience working with real-time fraud decisioning, transaction monitoring, and risk orchestration platforms such as Feedzai, Alloy, Accertify, Socure, Transmit Security, or comparable solutions.
- Demonstrated experience developing, tuning, testing, deploying, and monitoring fraud rules, decision strategies, risk scores, models, thresholds, and automated decisioning workflows.
- Strong understanding of fraud typologies, detection techniques, identity and behavioral risk signals, evolving fraud threats, control design, false-positive management, and the relationship between fraud risk, customer experience, operational efficiency, and business growth.
- Experience using data and performance metrics to evaluate fraud-control effectiveness, optimize decision strategies, and make risk-based recommendations.
- Working knowledge of machine learning, statistical modeling, feature engineering, and model performance concepts, with experience translating analytical outcomes into operational fraud controls.
- Strong proficiency in SQL and experience using analytics tools to perform data exploration, performance analysis, monitoring, and strategy optimization.
- Excellent written and verbal communication skills, including the ability to explain complex fraud, analytics, and decisioning concepts to technical, business, and executive stakeholders.
Preferred Qualifications
- Hands-on experience with Feedzai or a comparable real-time fraud decisioning platform, including rule configuration, model deployment, performance assessment, alert strategy, and production change management.
- Experience scaling fraud decisioning capabilities across multiple products, channels, geographies, client segments, or business units.
- Experience with model governance and validation practices, including champion/challenger testing, back-testing, simulation, performance monitoring, controlled rollouts, and ongoing strategy optimization.
- Knowledge of payments fraud and related attack vectors, including account takeover, identity fraud, synthetic identity, first-party fraud, authorized fraud, and insider-enabled fraud.
- Experience evaluating, onboarding, and measuring the effectiveness of third-party fraud, identity, device intelligence, behavioral risk, and consortium data providers, including proof-of-concept testing and signal optimization.
- Working knowledge of APIs, data pipelines, real-time event processing, and platform integrations, with the ability to effectively partner with technology and engineering teams.
- Experience applying AI/machine learning, graph analytics, network analysis, generative AI, or other advanced analytical techniques to fraud detection, risk management, and decisioning use cases.
- Experience establishing fraud analytics standards, strategy governance frameworks, model governance practices, and decisioning best practices within a large enterprise environment.
- Experience in financial services, payments, payroll, fintech, e-commerce, or another high-volume transaction environment.
- Advanced degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, Economics, or a related quantitative field.
- Demonstrated industry leadership through conference presentations, publications, patents, participation in fraud prevention forums, or contributions to the fraud, analytics, or risk management community.
What are you waiting for? Apply today!
Find out why people come to ADP and why they stay: https://youtu.be/ODb8lxBrxrY
(ADA version: https://youtu.be/IQjUCA8SOoA )
#LI-SD4
#LI-Hybrid
- Actual compensation will not be less than the applicable minimum wage or minimum exempt salary requirement under federal, state and local laws.
A little about ADP: We are a comprehensive global provider of cloud-based human capital management (HCM) solutions that unite HR, payroll, talent, time, tax and benefits administration and a leader in business outsourcing services, analytics, and compliance expertise. We believe our people make all the difference in cultivating a down-to-earth culture that embraces our core values, welcomes ideas, encourages innovation, and values belonging. We've received recognition for our work by many esteemed organizations, learn more at ADP Awards and Recognition.
Diversity, Equity, Inclusion & Equal Employment Opportunity at ADP: ADP is committed to an inclusive, diverse and equitable workplace, and is further committed to providing equal employment opportunities regardless of any protected characteristic including: race, color, genetic information, creed, national origin, religion, sex, affectional or sexual orientation, gender identity or expression, lawful alien status, ancestry, age, marital status, protected veteran status or disability. Hiring decisions are based upon ADP’s operating needs, and applicant merit including, but not limited to, qualifications, experience, ability, availability, cooperation, and job performance.
Ethics at ADP: ADP has a long, proud history of conducting business with the highest ethical standards and full compliance with all applicable laws. We also expect our people to uphold our values with the highest level of integrity and behave in a manner that fosters an honest and respectful workplace. Click https://jobs.adp.com/life-at-adp/ to learn more about ADP’s culture and our full set of values.
ADP is Hiring a Manager Data Science - Fraud
Job Summary
The Manager, Fraud Data Science is responsible for the performance, optimization, governance, and strategic expansion of ADP's Fraud Prevention Platform (FPP) and its associated fraud controls. This role owns the platform's real-time decisioning capabilities, including rules, models, decision strategies, third-party risk signals, AI-driven fraud detection capabilities, and control frameworks, ensuring they remain aligned with evolving fraud threats, enterprise risk appetite, client experience, and business growth objectives.
FPP is a foundational component of ADP's fraud strategy, and this role will help rapidly mature the platform, broaden its use cases, and expand its capabilities across ADP. The Manager will partner closely with other leaders across Global Fraud Prevention, Global Security, business teams, and engineering to translate emerging threats, fraud intelligence, investigative insights, and operational feedback into scalable, production-ready fraud controls that address an ever-changing fraud risk landscape.
This role will help shape the future direction of fraud decisioning at ADP. Success requires deep fraud prevention expertise, strong analytical and technical aptitude, experience with modern fraud decisioning platforms such as Feedzai, and the ability to balance fraud risk, client experience, operational efficiency, and business growth while continuously advancing the capabilities and adoption of ADP's enterprise fraud platform.
Like what you see? Apply now!
Learn more about ADP at tech.adp.com/careers
Key Responsibilities
- Own the strategy, performance, resilience, governance, and continuous evolution of the Fraud Prevention Platform (FPP) and its associated fraud controls.
- Drive real-time fraud decisioning capabilities, including rules, models, AI-enabled detection capabilities, decision strategies, and third-party risk signals to address evolving fraud threats and business needs.
- Monitor and optimize platform effectiveness through defined performance metrics, including fraud prevented, fraud losses, detection rates, false positive rates, operational impact, transaction volumes, and decision latency.
- Establish model and decisioning performance, testing, and experimentation frameworks, including champion-challenger strategies, A/B testing, simulation, controlled rollouts, and post-implementation validation to measure effectiveness, improve business outcomes, and support risk-based decision making.
- Develop closed-loop performance improvement processes that leverage fraud outcomes, production monitoring, threat intelligence, investigative findings, and operational feedback to continuously enhance fraud controls, decision strategies, and model performance.
- Oversee platform health and resilience, including uptime, transaction processing, integrations, third-party data providers, and system performance. Establish processes to rapidly identify and resolve service disruptions, data issues, and degraded control performance.
- Establish and maintain governance and change-management processes covering rule and model deployment, testing, approvals, documentation, monitoring, rollback, and ongoing performance management.
- Evaluate, optimize, and govern third-party fraud, identity, device intelligence, behavioral, consortium, and other risk data sources to maximize decision quality, fraud detection effectiveness, and return on investment.
- Define and execute the roadmap to mature FPP and expand fraud decisioning capabilities across products, channels, client segments, and enterprise use cases, including the adoption of advanced analytics and AI-driven capabilities.
- Partner within the Global Fraud Prevention group as well as with Global Security, Engineering, and business stakeholders to translate fraud risks and requirements into scalable platform capabilities and measurable business outcomes.
- Serve as the enterprise subject matter expert for FPP, advising stakeholders on platform capabilities, decisioning strategies, AI/ML-enabled functionality, integrations, data requirements, and fraud control effectiveness.
To Succeed in This role
- You'll have a bachelor’s degree or equivalent practical experience in risk management, fraud analytics, data science, computer science, statistics, engineering, or a related field.
Required Qualifications
- 5-7+ years of experience in fraud prevention, fraud detection, risk decisioning, payments risk, identity risk, financial crime, or related discipline.
- Experience with Python is required.
- Experience working with real-time fraud decisioning, transaction monitoring, and risk orchestration platforms such as Feedzai, Alloy, Accertify, Socure, Transmit Security, or comparable solutions.
- Demonstrated experience developing, tuning, testing, deploying, and monitoring fraud rules, decision strategies, risk scores, models, thresholds, and automated decisioning workflows.
- Strong understanding of fraud typologies, detection techniques, identity and behavioral risk signals, evolving fraud threats, control design, false-positive management, and the relationship between fraud risk, customer experience, operational efficiency, and business growth.
- Experience using data and performance metrics to evaluate fraud-control effectiveness, optimize decision strategies, and make risk-based recommendations.
- Working knowledge of machine learning, statistical modeling, feature engineering, and model performance concepts, with experience translating analytical outcomes into operational fraud controls.
- Strong proficiency in SQL and experience using analytics tools to perform data exploration, performance analysis, monitoring, and strategy optimization.
- Excellent written and verbal communication skills, including the ability to explain complex fraud, analytics, and decisioning concepts to technical, business, and executive stakeholders.
Preferred Qualifications
- Hands-on experience with Feedzai or a comparable real-time fraud decisioning platform, including rule configuration, model deployment, performance assessment, alert strategy, and production change management.
- Experience scaling fraud decisioning capabilities across multiple products, channels, geographies, client segments, or business units.
- Experience with model governance and validation practices, including champion/challenger testing, back-testing, simulation, performance monitoring, controlled rollouts, and ongoing strategy optimization.
- Knowledge of payments fraud and related attack vectors, including account takeover, identity fraud, synthetic identity, first-party fraud, authorized fraud, and insider-enabled fraud.
- Experience evaluating, onboarding, and measuring the effectiveness of third-party fraud, identity, device intelligence, behavioral risk, and consortium data providers, including proof-of-concept testing and signal optimization.
- Working knowledge of APIs, data pipelines, real-time event processing, and platform integrations, with the ability to effectively partner with technology and engineering teams.
- Experience applying AI/machine learning, graph analytics, network analysis, generative AI, or other advanced analytical techniques to fraud detection, risk management, and decisioning use cases.
- Experience establishing fraud analytics standards, strategy governance frameworks, model governance practices, and decisioning best practices within a large enterprise environment.
- Experience in financial services, payments, payroll, fintech, e-commerce, or another high-volume transaction environment.
- Advanced degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, Economics, or a related quantitative field.
- Demonstrated industry leadership through conference presentations, publications, patents, participation in fraud prevention forums, or contributions to the fraud, analytics, or risk management community.
What are you waiting for? Apply today!
Find out why people come to ADP and why they stay: https://youtu.be/ODb8lxBrxrY
(ADA version: https://youtu.be/IQjUCA8SOoA )
#LI-SD4
#LI-Hybrid
- Actual compensation will not be less than the applicable minimum wage or minimum exempt salary requirement under federal, state and local laws.
A little about ADP: We are a comprehensive global provider of cloud-based human capital management (HCM) solutions that unite HR, payroll, talent, time, tax and benefits administration and a leader in business outsourcing services, analytics, and compliance expertise. We believe our people make all the difference in cultivating a down-to-earth culture that embraces our core values, welcomes ideas, encourages innovation, and values belonging. We've received recognition for our work by many esteemed organizations, learn more at ADP Awards and Recognition.
Diversity, Equity, Inclusion & Equal Employment Opportunity at ADP: ADP is committed to an inclusive, diverse and equitable workplace, and is further committed to providing equal employment opportunities regardless of any protected characteristic including: race, color, genetic information, creed, national origin, religion, sex, affectional or sexual orientation, gender identity or expression, lawful alien status, ancestry, age, marital status, protected veteran status or disability. Hiring decisions are based upon ADP’s operating needs, and applicant merit including, but not limited to, qualifications, experience, ability, availability, cooperation, and job performance.
Ethics at ADP: ADP has a long, proud history of conducting business with the highest ethical standards and full compliance with all applicable laws. We also expect our people to uphold our values with the highest level of integrity and behave in a manner that fosters an honest and respectful workplace. Click https://jobs.adp.com/life-at-adp/ to learn more about ADP’s culture and our full set of values.
Senior Data Science Engineer Jobs by Experience Level
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Find JobsSenior Data Science Engineer Job Market
Who's Hiring
- Amazon32
- Capital One18
- Walmart16
- Deloitte12
- TikTok10
Top Industries Hiring
- Technology & Software44
- Education22
- Retail13
- Banking & Financial Services13
- Insurance9
What Employers Look For
The qualifications that appear most often in senior data science engineer jobs.
- 5 or more years of experience in data science, machine learning, or a related engineering discipline
- Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- Hands-on experience designing and deploying production-grade machine learning pipelines
- Strong command of distributed computing tools including Spark, Databricks, or equivalent platforms
- Experience with cloud platforms such as AWS, Google Cloud, or Azure for data and model infrastructure
- Bachelor's or master's degree in computer science, statistics, mathematics, or a closely related field
Tips for Your Senior Data Science Engineer Job Search
Quantify your modeling impact clearly
Hiring managers want to see what your models actually moved. Replace vague claims with specifics: latency reduced, prediction accuracy improved, or revenue attributed to a deployed system. Concrete outcomes separate senior candidates from mid-level ones faster than any credential.
Tailor your resume to the stack
Senior data science engineer roles vary widely by tooling. One company runs Spark on Databricks, another on Kubernetes with Ray. Scan each job posting for the exact stack and mirror that language in your resume so automated screening and hiring managers both register the match.
Target roles by architecture ownership
Some postings want someone to build pipelines, others want someone to own the full ML platform. Read the responsibilities section carefully and apply to roles where the scope matches what you can lead independently, not just contribute to.
Apply early to roles that fit
Migrate Mate lists senior data science engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare a system design walkthrough
Most senior-level loops include a machine learning system design round. Practice walking through feature stores, serving infrastructure, monitoring, and retraining pipelines out loud. Interviewers are testing whether you think end-to-end, not just whether you can tune a model.
Negotiate scope before you negotiate compensation
At this seniority level, title scope and team ownership vary more than the pay band. Before your final conversation, clarify what decisions you'd own, whether you'd manage engineers, and how success gets measured. Misaligned scope costs more than a lower offer over time.
Senior Data Science Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most senior data science engineers?
The companies hiring the most senior data science engineers right now include Amazon, Capital One, and Walmart, with the largest share of openings in California, New York, and Virginia, based on current listings on Migrate Mate as of September 2026. Demand is especially high at companies scaling ML infrastructure across cloud-native platforms.
How many senior data science engineer jobs are remote?
About 65% of senior data science engineer openings are fully remote or hybrid as of September 2026, making this one of the more flexible senior technical roles in the market. ML platform engineering and research-adjacent positions tend to carry the highest share of fully remote arrangements compared to roles tied to real-time production systems.
How do you become a senior data science engineer?
You reach the senior level by owning production machine learning systems end-to-end, not just building models in notebooks. That means shipping models that run reliably in production, learning pipeline orchestration and feature engineering at scale, and contributing to architectural decisions. Most engineers get there by taking on increasing infrastructure ownership over several years and demonstrating that their work directly influences business outcomes.
Can you get hired as a senior data science engineer with limited experience?
It's possible if you can demonstrate depth in a specific area that is hard to find. Strong open-source contributions, a portfolio of production ML projects, or deep expertise in a specialized domain like recommendation systems or computer vision can offset a shorter work history. Targeting startups or smaller teams where scope is broader and seniority expectations are more flexible also improves your chances significantly.
What does the senior data science engineer interview process look like?
The process typically includes a recruiter screen, a technical phone interview covering ML concepts and coding, a machine learning system design round, and a full loop with several panel interviews. The system design round is the stage where senior candidates are most frequently filtered out. Expect to walk through how you would architect a complete ML system, including data ingestion, training, serving, and monitoring, rather than just solve a modeling problem.
Where can I find and apply to senior data science engineer jobs?
You can find and apply to senior data science engineer jobs on Migrate Mate, which lists current openings from across the United States in one place. Find roles that match your experience and specialization, then apply directly to each listing.
See All 648+ Senior Data Science Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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