Responsibilities include, but are not limited to:
- Coordinate with all levels of management to understand and devise possible solutions for predictive analytics.
- Create and deploy machine learning models for business objectives in department workflows.
- Maintain models both internally and cloud based.
- Regularly evaluate models using theoretical methods.
- Keep up to date with latest industry trends and techniques.
- Provide support for all departments with analytical and predictive needs.
- Prepare needed regulatory reports needed based on Machine Learning Models.
- Explore and implement AI-Driven solutions to business problems.
- Streamline data-driven processes using automation.
- Analyze financial information to determine present and future financial performance.
- Perform complex analysis in an evolving data environment.
- Establish databases of pertinent information for use in analyzing plans and forecasts.
- Coordinate with all levels of management to gather, analyze, summarize, and prepare recommendations regarding financial plans, acquisition activity, new business planning, trended future requirements, government requirements, and operating forecasts.
- Analyze peers.
- Assist with special projects.
Requirements- Well-developed business acumen.
- Ability to multi-task and handle numerous assignments simultaneously.
- A process thinker seeking productivity and exceptional service.
- Excellent verbal, telephone, and written communication skills.
- Ability to work well in a team environment.
- A professional, positive and enthusiastic attitude.
- Advanced computer skills.
- Excellent listening and feedback skills.
- Good problem solving skills.
- Effective training skills.
Desired Qualifications and Skills- Bachelor's degree or equivalent education and experience in a relevant field.
- Familiarity with container-based services, such as AWS App Runner, S3, ECS, etc.
- Strong skills in Python & SQL.
Key Measurement Metrics- All models in production outperform the time / value tradeoff of manual decision
- Assessment of model performance at least quarterly
- Models are updated at least quarterly or as needed
- Notification established of underperforming models; models are improved or decommissioned.
- Assessment of AI tools and processes on a regular basis to ensure accuracy and effectiveness.
