Data Science Manager-Credit Risk Modeling

This range is provided by Harnham. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$170,000.00/yr - $200,000.00/yr

Location: Dallas (Addison) - Hybrid - 2 Days per week in office.

This is a fintech company that provides products focused on managing everyday expenses. The Data Science team applies advanced statistical and machine learning techniques to create predictive models used in Underwriting, Account Management, and Operations. They are looking for a Data Science Manager to lead a small team.

Responsibilities:

• Lead a team of data scientists to deliver predictive models, risk models, and analytical solutions that drive business value.
• Design, develop, and deploy machine learning algorithms for use across various functions including Underwriting, Marketing, and Operations.
• Collaborate with business partners to meet goals and support various teams and portfolios.
• Communication is very important for this role. This individual will present findings and communicate with execs and stakeholders, as well as with their team.
• Process and analyze large datasets using tools like Python, Spark, and Snowflake.
• Implement and test machine learning algorithms for risk management in acquisition channels.
• Apply data mining techniques to minimize credit/fraud losses and optimize product profitability.
• Manage the implementation of scoring models on decision platforms, including the cloud.
• Provide expertise on third-party data sources (e.g., TransUnion, Experian) and guide effective data usage.
• Document models and processes using tools like Jupyter Notebook and Rmarkdown.

Qualifications:

• Master's degree in a quantitative field (Statistics, Economics, Mathematics, Engineering, etc.); Ph.D. preferred.
• At least six years of experience in data science or risk modeling, with three years in a leadership role.
• Expertise in machine learning techniques (Random Forest, Gradient Boosting, LASSO, etc.).
• Strong data manipulation and engineering skills.
• Proficiency in Python, R, Java, Linux, and big data tools (e.g., Spark, Hadoop, Snowflake).
• Experience with database technologies (NoSQL, JSON/XML parsing, etc.).
• Excellent communication skills and the ability to work in a fast-paced environment.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Finance