Senior Data Scientist, ML— Fraud Detection & Effectiveness

Adobe; New York (NY); Hybrid; USD 163,000 - 236,000; Full time

Benefits offered by this job

Hybrid work model

Excellent benefits

Job summary

Adobe is seeking a Senior Data Scientist, ML—Fraud Detection & Effectiveness in New York to build fraud and abuse detection models and quantify their business impact. You will own end-to-end modeling—from ground-truth definition and feature engineering to evaluation, monitoring, and executive-ready dashboards that explain results.

This is a hybrid role with minimum three days in the office. The role requires 8+ years in applied DS/ML, strong Python and SQL, and the ability to turn complex

Qualifications

• 8+ years in applied Data Science/ML with production models.
• Strong Python and SQL skills with large datasets.
• Experience with model evaluation, drift, and monitoring.
• Ability to translate analysis into actionable business insights.

Responsibilities

• Build and tune ML models for fraud and abuse detection.
• Develop evaluation frameworks and metrics for performance measurement.
• Analyze false positives/negatives, model drift, and emerging patterns.
• Define ground-truth labeling approaches and fraud taxonomies.
• Design experiments and evaluate tradeoffs across precision, recall and business impact.
• Build dashboards translating detection performance into business insights.
• Identify data quality issues and leakage sources.
• Collaborate with engineering, product, policy, and risk teams to implement improvements.

Skills

Python

SQL

Statistics

Experimentation

Data visualization

Communication

Education

Bachelor's in Statistics/Math/CS or related field

Advanced degree (MS/PhD) preferred

Job description

Senior Data Scientist, ML— Fraud Detection & Effectiveness

The Opportunity

We are seeking an experienced Senior Machine Learning Data Scientist to build fraud and abuse detection models and measure how effectively they work. This role combines hands-on modeling with deep experimentation, evaluation, and analytics to improve detection and quantify business impact.

You will work across the fraud lifecycle — from modeling and ground-truth definition to performance measurement, monitoring, and executive-ready insights!

What you'll Do

• Build and tune ML models for fraud and abuse detection using statistical and classical ML techniques.
• Develop robust evaluation frameworks, datasets, and metrics to measure model and mitigation effectiveness.
• Analyze false positives/negatives, model drift, and emerging fraud patterns to continuously improve detection.
• Define ground truth, labeling approaches, and fraud taxonomies that support reliable model development and evaluation.
• Design experiments and evaluate tradeoffs across precision, recall, customer impact, and fraud loss.
• Build dashboards and metrics that translate detection performance into measurable business impact.
• Pressure-test models and data for leakage, bias, data-quality issues, and other sources of misleading results.
• Partner across engineering, product, policy, and risk teams to turn insights into detection improvements and business decisions.

What you'll need to succeed

• 8+ years in applied Data Science / ML, with experience building and evaluating production ML models.
• Strong foundation in statistical and classical ML, experimentation, model evaluation, and performance measurement.
• Strong hands-on Python and SQL skills working with large, complex datasets.
• Experience with model monitoring, drift, false-positive/false-negative analysis, and imperfect or delayed labels.
• Strong data visualization and storytelling skills — able to translate complex analysis into clear insights and recommendations.
• Strong analytical judgment, ownership, and ability to operate independently through ambiguity.
• Bachelor's or equivalent experience in Statistics, Mathematics, Computer Science, or related field; advanced degree a plus.

Preferred Attributes

• Experience in fraud, abuse, risk, identity, trust & safety, or other adversarial domains.
• Experience with anomaly detection, clustering, behavioral modeling, or prevalence estimation.
• Experience with labeling frameworks, weak supervision, active learning, or human-review systems.
• Familiarity with LLMs and AI-assisted evaluation/analysis.
• Experience evaluating multi-layered risk controls and automated decisioning systems.

Hybrid Work Model: This role follows a hybrid schedule, with a minimum of 3 days per week in the office.

About Adobe

Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.

Our 30,000+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours.

Let’s Adobe together

At Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Learn more about Adobe life, including our values and culture, focus on people, purpose and community, Adobe for All, comprehensive benefits programs, the stories we tell, the customers we serve, and how you can help us advance our mission of empowering everyone to create.

Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. Learn more.

Adobe aims to make our Careers website and recruiting process accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email [e-mail].

AI Use Guidelines for Interviews:
Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.

At Adobe, we empower employees to innovate with AI — and we look for candidates eager to do the same. As part of the hiring experience, we provide clear guidance on where AI is encouraged during the process and where it’s restricted during live interviews. See how we think about AI in the hiring experience.

Expected Pay Range:

Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $133,100 -- $236,400 annually. Pay within this range varies by work location and may also depend on job‑related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process.In California, the pay range for this position is $163,200 - $236,400In New York, the pay range for this position is $163,200 - $236,400In Illinois, the pay range for this position is $149,100 - $216,000In Washington, the pay range for this position is $157,900 - $228,575

At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).

In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.

State‑Specific Notices:

California:

Fair Chance Ordinances

Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.

Colorado:

Application Window Notice

There is no deadline to apply to this job posting because Adobe accepts applications for this role on an ongoing basis. The posting will remain open based on hiring needs and position availability.

Massachusetts:

Massachusetts Legal Notice

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.