Data Scientist

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.