Job description

What will your day look like?

The Data Scientist is responsible to lead the development, validation, documentation, and implementation of statistical and advanced machine learning models that are used for decisioning and product management throughout member lifecycle, including segmentation, optimization, and prescriptive analytics; work effectively with internal clients to understand the business needs, propose appropriate quantitative solutions, and develop data-driven recommendations to improve member experience and achieve business objectives; provide statistical and analytical inputs for the development and implementation of policies and business plans related to loan origination, portfolio management, loss forecasting and portfolio monitoring; proactively collaborate with cross-functional partners including Business Intelligence, Model Risk Management, and Information Technology (Database Engineers, Application Development and Information Security); and deliver effective presentations of analysis results and recommendations to multiple levels of leadership and business partners. The incumbent is a key contributor to the success of the Risk Analytics team, with the general supervision provided by the Manager, Decision Science.

Do you see yourself doing this?

Use advanced analytics to solve business problems and support business and risk strategic objectives. Collaborate with various partners to prioritize requests/needs and provide a holistic view of the analysis.

Perform advanced data analytics (e.g. data mining, statistical analysis, predictive analytics), and develop, document, and support the deployment of quantitative models, which are used for decisioning and product management through member lifecycle including acquisition, activation, utilization, relationship deepening and retention.

The risk analytics projects may include but not be limited to: automated credit decisioning, risk rating models, pricing models, loan loss forecasting, as well as CECL models and validation.

Acquire and integrate data from multiple data sources for analysis, perform exploratory to advanced predictive and/or modeling analytics, and identify data relationships such as trends, patterns and correlations in order to solve business questions as well as provide actionable recommendations.

Leverage multiple complex data sources such as credit bureau reports, and customer supplied information at large scale to optimize approve / decline and credit line assignment decisions.

Be able to presents data and analysis in a clear and concise manner allowing the internal clients to quickly understand the results and make data driven decisions.

Work jointly with engineers to deploy models into production environment.

Perform model validation and documentation of newly-developed predictive models to ensure they follow best industry practices and are in compliance with internal and regulatory requirements.

Conducting annual model validations to ensure models are working as intended and proactively seek, build and consolidate new data inputs to improve model performance.

Promote a risk-aware culture, ensure efficient and effective risk and compliance management practices by adhering to required standards and processes.

Adhere to and ensure compliance of all business transactions with policy and process of the Bank Secrecy Act. Comply with Privacy Act directives and all other applicable state and federal laws, company procedures and policies. Maintain integrity and ethics in all actions and conversations with or regarding credit union members and their accounts.

What makes you a great fit?

You’ll be a great fit if in addition to an advanced degree (M.S./PhD) in Mathematics, Statistics, Quantitative Finance, Engineering, Computer Science, or other quantitative field required, and you have:

Require 2+ years of data analytics and modeling experience in the financial services industry (consumer lending preferred)

Hands-on work experience on building and deploying decisioning models that are used for one or more of the areas in credit risk, collections, marketing, or fraud

Expert knowledge in one of the statistical programming languages such as Python or R, and database languages such as SQL

Basic knowledge on lending, credit risk measures (PD, LGD and EAD)

Excellent communication skills and ability to present complex analyses and technical subject matter clearly and concisely to internal customers with different technical backgrounds

Strong teamwork and interpersonal skills to collaborate with people across functions

Self-motivated and impact-oriented

When you’re happy, we’re happy!

As a thank you for joining our team, you’ll benefit from:
Competitive medical, dental, and free vision benefits

Competitive compensation plan

Contributions towards gym memberships

Generous PTO and banking holidays off

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