Data Science Lead- Fraud Modeling

Description

Citizens Bank is seeking a Data Science Lead to spearhead enterprise fraud modeling initiatives. This role combines hands-on technical expertise with leadership responsibilities, driving innovation and ensuring high-quality model development in a regulated environment.

Day-to-Day Responsibilities

• Lead the design, planning, and development of fraud detection ML models

• Supervise and mentor a team of data scientists & data analysts, fostering technical growth and collaboration

• Act as a thought leader, introducing new methodologies and technologies to enhance modeling capabilities

• Participate in hands-on coding and modeling activities

• Maintain a well-organized, high-quality codebase—and enforce best practices in version control

• Contribute to strategic visioning and translate plans into actionable steps for the team

• Engage with stakeholders to provide consultative insights and regular progress updates

• Build and maintain business relationships with data engineering and fraud strategy teams

• Maintain project timelines and deliverables 

Characteristics of a Competitive Candidate

• Extensive experience in and comprehensive knowledge of Fraud Strategy analytics or modeling 

• Accomplished individual contributor 

• Proactive and self-driven—with a strong sense of ownership

• Excellent communication & interpersonal skills – able to build trust, influence decisions, and navigate cross-functional dynamics

• Highly organized and detail oriented — holds self and others to high standard of quality

• Strategic thinker who stays current with emerging trends and integrates them into daily work

• Strong leadership qualities and experience: able to inspire team toward a vision and build an effective culture of excellence 

• Passionate about data and fraud prevention; has a contagious curiosity 

Key Requirements

• 5+ years of experience in fraud modeling or strategy

• Demonstrated ability to lead teams and mentor junior colleagues

• Strong communication skills, including presentations and deck creation

• Experience engaging with model risk governance in a regulated institution

• Expertise in handling large-scale datasets and real-time time series modeling

• Hands-on experience building machine learning and deep learning models for fraud detection

Technical Skills

• Cloud Experience (AWS): At least 5 years’ experience

• Python or SAS: Expert level

• SQL: Expert Level

• PySpark: Intermediate to Expert

• GitHub / BitBucket: Intermediate to Expert

• Neo4J: Preferred

• Apache Flink: Nice to have

Education

• Ph.D. in Engineering, Statistics, Computer Science, Data Science, Mathematics, or Operations Research (Preferred)

• Master’s degree in one of the above fields (Minimum)

Hours & Work Schedule

• Hours per Week: 40
• Work Schedule: Mon-Friday

Some job boards have started using jobseeker-reported data to estimate salary ranges for roles. If you apply and qualify for this role, a recruiter will discuss accurate pay guidance.

Equal Employment Opportunity

Citizens, its parent, subsidiaries, and related companies (Citizens) provide equal employment and advancement opportunities to all colleagues and applicants for employment without regard to age, ancestry, color, citizenship, physical or mental disability, perceived disability or history or record of a disability, ethnicity, gender, gender identity or expression, genetic information, genetic characteristic, marital or domestic partner status, victim of domestic violence, family status/parenthood, medical condition, military or veteran status, national origin, pregnancy/childbirth/lactation, colleague’s or a dependent’s reproductive health decision making, race, religion, sex, sexual orientation, or any other category protected by federal, state and/or local laws. At Citizens, we are committed to fostering an inclusive culture that enables all colleagues to bring their best selves to work every day and everyone is expected to be treated with respect and professionalism. Employment decisions are based solely on merit, qualifications, performance and capability.