Senior Specialist - Data Science - Fraud Analytics. Austin LilyLifestyle

General information

# 22089

Remote? No

Ally Financial only succeeds when its people do - and that s more than some cliché people put on job postings. We live this stuff! We see our people as, well, people - with interests, families, friends, dreams, and causes that are all important to them. Our focus is on the health and safety of our teammates as well as work-life balance and diversity and inclusion.

From generous benefits to a variety of employee resource groups, we strive to build paths that encourage employees to stretch themselves professionally. We want to help you grow, develop, and learn new things. You re constantly evolving, so shouldn t your opportunities be, too?

At this time, Ally will not sponsor a new applicant for employment authorization for this position. In office role, Ally work location is Lewisville TX or Charlotte NC

As a key member of the Fraud Analytics, Strategies and Trends (FAST) team, the Sr Specialist - Data Scientist owns the performance and continuous optimization of vendor-owned fraud detection models. This role partners closely with vendors and internal stakeholders to monitor model outcomes, tune thresholds and strategies, validate model changes, and ensure solutions remain aligned to business goals and emerging fraud trends.

In addition, the role delivers ad hoc advanced analytics and recommendations that inform fraud policy and procedural improvements.

• Manage, monitor, and optimize external fraud detection models provided by third-party vendors
• Collaborate with vendors to maintain model performance, adjust thresholds, and ensure accuracy in detecting fraudulent activities
• Create and maintain regular reports and dashboards to track model performance, detect trends, and identify key performance indicators
• Assess and validate vendor model updates or changes, ensuring alignment with business needs and fraud detection objectives
• Perform regular audits and reviews of model output, providing insights into effectiveness and recommending improvements
• Lead or contribute to the development of new vendor model initiatives or data-driven tools to enhance fraud detection abilities
• Leverage advanced statistical and machine learning techniques to analyze large, complex datasets and generate actionable insights
• Develop analysis to support potential policy and/or procedural changes to reduce fraud and improve the customer s experience, and provide recommendations based on findings
• Develop new techniques for fraud analysis, as well as recommendations for internal FAST policies and/or procedures to help drive efficiencies and repeatable quality results.
• Support the continuous enhancement of fraud detection systems and policies to proactively mitigate risks
• Develop strong relationships with other FAST team members to drive collaboration, shared insights across the team, as well as coaching and mentoring peers toward improvement.
• Develop strong relationships with key business partners both inside and outside Fraud Prevention, pursuing solution delivery and shared successes.
• Deploy findings of analysis through effective presentation skills and use of influence.
• Deploy thorough controls that include not only well-developed countermeasures, but also detailed change control and backup documentation to ensure controls are understood and sustained.

Minimum Qualifications

• 3+ years of relevant experience
• Bachelor s degree in Data Science, Analytics, Computer Science, Economics, Statistics, Mathematics, Engineering or similar field of study; or equivalent

Preferred Qualifications

• 3+ years of experience using advanced data manipulation techniques to solve business problems
• Experience managing the lifecycle of predictive models, including risk assessment, performance validation, and ensuring compliance with governance and regulatory standards
• Data preparation programs such as SQL, Python or R.
• Solid understanding statistical and machine learning programming tools such as Python and R.
• Proficiency in various database environments, such as Snowflake, Hadoop, or Oracle and proven experience to extract data.
• Knowledge of linear and logistic regression, time series analysis, decision trees, survival analysis, ensemble trees (gradient boosting, random forest), and other quantitative techniques.
• Data visualization skills through Power BI (Preferred) or Tableau.
• Ability to analyze complex data, deriving clear action-oriented recommendations for the business, and communicating insights clearly and concisely to individuals from diverse backgrounds.
• Comfort level with ambiguity and ability to manage multiple projects at the same time.
• Degree in Data Science, Analytics, Computer Science, Economics, Statistics, Mathematics, Engineering or similar field of study (Master s or PhD preferred).

#LI-Hybrid

Ally s compensation program offers market-competitive base pay and pay-for-performance incentives (bonuses) based on achieving personal and company goals.…