Sr. Fraud Data Science Engineer

Our Company
Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. 
We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours!

The Sr. Fraud Data Science Engineer uses their technical skills to detect and predict emerging fraud trends, automate processes, and provide data driven insights. The candidate has an innovative approach with passion for fraud detection and prevention along with creativity in data visualization. The scientist will work with different partners across Adobe to develop best in class fraud detection capabilities. 

What you'll Do 
Develop ML models to predict and detect fraud from historical and emerging trends across various platforms and products. 
Automate manual workflows to build efficiencies and scale up fraud reviews, investigations and operational workflows. 
Expert in data analyses and improve tools and processes through technical expertise. 
Excellent data visualization skills with ability to tell the story to key partners. 
Translate investigations and intel into root cause analysis to find opportunities for scale. 

What you need to succeed 
Advanced degree (Masters or Ph.D.) in Computer Science, Statistics, Mathematics, or related quantitative field, or equivalent work experience with a Bachelor's degree. 

6+ years of proven experience in data science, machine learning, with a focus on fraud detection or risk management in subscription-based businesses or financial services. 

Expertise in machine learning techniques, including supervised and unsupervised learning, anomaly detection, and ensemble methods. 
Proficiency in programming languages such as SQL, Python or R. 
Strong understanding of big data technologies and distributed computing frameworks (e.g., Hadoop, Spark) for handling large-scale datasets. 
Exposure to using Ai agents and LLM (Large Language Model) for any of the projects. 
Excellent communication and leadership skills, with the ability to collaborate effectively with cross-functional teams and drive alignment on fraud detection strategies. 
Proven track record of leading complex data science projects from conception to production deployment, delivering tangible business value. 
Experience with fraud detection tools and platforms (e.g., fraud detection APIs, fraud management systems) is highly recommended. 

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 $127,300 -- $229,300 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.

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.

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

Adobe is proud to be an Equal Employment Opportunity and affirmative action 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 applicable characteristics protected by law. Learn more.
 

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