Senior Specialist - Data Science - Fraud Analytics

General Information

Reference #: 22089

Remote? No

Location:

Lewisville, TX or Charlotte, NC (in‑office)

Ally will not sponsor work authorization for this position.

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. The 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.

The role also 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 a related field; 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 of statistical and machine learning programming tools such as Python and R.
• Proficiency in various database environments, such as Snowflake, Hadoop, or Oracle, with proven experience extracting 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 with ambiguity and ability to manage multiple projects simultaneously.
• Degree in Data Science, Analytics, Computer Science, Economics, Statistics, Mathematics, Engineering or a related field (Master's or PhD preferred).

Compensation: market‑competitive base pay with bonus incentives based on…