Data Scientist - Credit Analytics

Farmer Mac is seeking a highly motivated data scientist with experience in credit risk analytics to fill a dynamic role in the Credit department. This Data Scientist will use data science, statistics, and machine learning techniques to develop credit risk models and portfolio credit risk reporting solutions that provide actionable insights to stakeholders.

The Data Scientist – Credit Analytics is responsible for planning, executing, and maintaining data science projects. The Data Scientist will work closely with the Director – Credit Analytics and key stakeholders in the Credit, Finance, and Enterprise Risk Management departments, as well as Farmer Mac’s Business Lines to evaluate the performance of existing credit models and develop new models for Farmer Mac’s agricultural real estate and corporate loan portfolios.

The role will also work with key stakeholders to understand their business needs and collaborate with the Sr. Data Analyst – Credit Analytics to identify data science and automation solutions to deliver key portfolio credit risk reporting.

The role is expected to quickly contribute to Farmer Mac’s Credit Analytics function.

A suitable candidate must be a proactive self-starter who can contribute independently. The Data Scientist – Credit Analytics must also be detail-oriented, resourceful, and capable of producing high-quality work among competing priorities.

The Data Scientist - Credit Analytics will be an integral part of the Credit Analytics team, reporting directly to the Director - Credit Analytics, and working closely with the Credit team and key stakeholders in Finance, Enterprise Risk Management, and the Business Line. This role will have a direct impact on Farmer Mac’s credit risk models and portfolio credit risk reporting products, with visibility to senior management.

Farmer Mac embraces a Presence-with-Purpose work environment, which allows for flexible work locations while providing opportunities for teams to come together in the office with purpose. The position will be based in Farmer Mac’s Washington, D.C. office, Johnston, IA, office or remotely if outside of those two metropolitan areas. Work outside core hours may be required for planned and unplanned activities, including completing time-sensitive projects and attending off-site meetings or events.

• Lead projects to understand credit risk stakeholder needs, research portfolio credit risk trends, and use data science techniques to provide actionable insights
• Develop, evaluate, and maintain credit risk models and other data science solutions to understand and project portfolio credit risk
• Conduct ongoing model monitoring and performance evaluation
• Develop and maintain advanced portfolio credit risk dashboards
• Develop expertise in Farmer Mac’s data systems to support the creation of data sets suitable for data science product development and contribute to Farmer Mac’s enterprise data quality initiatives
• Recommend analytical and data process improvements
• Develop and maintain procedures and documentation for assigned projects
• Follow Farmer Mac’s model risk management and AI governance best practices
• Provide support to end users of the Credit Analytics team’s models and reports

• Strong knowledge of statistics, machine learning, econometrics, time series, and related fields
• Domain knowledge of lending, economics, finance, or related fields
• Knowledge of model validation and model risk management principles and expectations
• Python (preferred) or R, SQL, git, and related programming skills
• Research skills
• Ability to communicate technical topics to audiences with varied backgrounds
• Experience using Power BI (preferred), Tableau, or similar visualization tools
• Self-starter with excellent critical thinking and communication skills
• Experience writing well-documented, reusable code and robust model documentation

• Three to seven years of professional data science, credit modeling, econometric analysis, research, or related experience, preferably in roles with exposure to commercial,…