We are seeking a talented Statistical Modeler to join our team. In this role, you will be crucial in developing an alternative credit score with a data model that extends beyond traditional lending, encompassing employee wellness, stress levels, workplace morale, homeownership, and more. This model will be pegged to credit scores to revolutionize lending practices and promote financial inclusion.
**Responsibilities:**
- Collaborate with cross-functional teams to define the scope and objectives of the alternative data model for employee wellness and non-traditional lending.
- Research and analyze various factors impacting employee wellness, stress levels, workplace morale, homeownership, and other non-traditional variables.
- Develop statistical models to assess employee wellness and predict factors affecting non-traditional lending decisions.
- Normalize and scale each factor to create a comprehensive scoring system pegged to credit scores, similar to the FICO Credit Score.
- Implement algorithms to handle missing data and ensure the model's accuracy and reliability across multiple domains.
- Validate and refine the model through iterative testing and validation processes, incorporating feedback from stakeholders.
- Collaborate with data engineers and software developers to integrate the model into and employee wellness programs.
- Stay updated on industry trends and best practices in statistical modeling, employee wellness, and non-traditional lending to drive continuous improvement.
**Requirements:**
- Bachelor's degree in Statistics, Mathematics, Computer Science, Economics, or a related field. Master's or Ph.D. preferred.
- Proven experience (X years) in statistical modeling, preferably in the financial industry or employee wellness space.
- Strong understanding of credit scoring principles, employee wellness metrics, and non-traditional lending variables.
- Proficiency in statistical analysis tools and programming languages such as R, Python, or SAS.
- Excellent analytical and problem-solving skills with a keen attention to detail.
- Ability to communicate complex ideas and findings effectively to both technical and non-technical stakeholders.
- Experience with machine learning techniques and algorithms is a plus.
- Ability to thrive in a fast-paced, collaborative environment and adapt to changing priorities.