Sr data Scientist

The Senior Data Scientist is responsible for using programming, mathematical, statistical, and analytical skills to solve complex business problems. This role will advance company-wide understanding and implementation of machine learning models. The Senior Data Scientist will present findings to the business or clients and must be comfortable communicating to all levels of the organization.

Duties and responsibilities

Creating Value Through Data (15%)

• Produce clear, insightful, understandable work products (visualizations, reports, presentations, etc.) to outline business opportunities and actionable recommendations.• Transform data into insights through leveraging internal and external tools (e.g., Python, GitHub, IDE, Spark, R, SQL, Excel, Power BI, etc.) to identify and quantify opportunities.• Build studies that add substantial value to the decision-making process through proactive data analysis, reporting, and research.

Model Development (40%)

• Develops machine learning solutions for complex business problems; examine data and develop use cases to identify artificial intelligence/machine learning solutions.• Provides feedback to product and engineering teams on machine learning solution design and development and communicate the approach and implementation of the machine learning solution.• Works independently with mentorship from senior team members and/or leadership of the Data Science team on leveraging existing frameworks and models for known use cases around machine learning.• Devises new algorithmic approaches to solving difficult quantitative problems using large scale enterprise data sources.

Communicating Insights (10%)• Presents analysis and resulting recommendations to senior management.• Leverages data to present compelling business cases to optimize investments and operations.• Communicates and educates both technical and non-technical employees on analytics and data-driven decision making.

Project Management (15%)

• Leads projects and assists in development of project plans, conducting analysis/modeling, hypothesis testing, presenting complex information for various audiences in simplified terms, and identifying next steps and future opportunity• Mentors team members in technical proficiency, code reviews, and business acumen• Collaborates with team members to prioritize requests requiring multiple resources for thorough project completion within stated timelines.

Support Data Strategies (10%)

 Remains abreast of developments in the field(s) of insurance, management, and data sciences by attending self-development programs, interacting with peers, and reviewing pertinent literature. Incorporates advancements when practicable and cost effective.

 Participate and drive data modeling and governance best practices.

 Proactively engages internal and external teams to discover areas of analytical needs.

 Advances company-wide understanding and implementation of AI and machine learning as well as matures the team’s practices and procedures, leveraging learnings from existing implementations.

 Participates in the talent acquisition process by screening and interviewing candidates at all levels.

Product Leadership (10%)

 Work cross-functionally with business owners to develop innovative advanced analytics products that will increase customer experience, capitalize growth opportunities, deliver competitive advantage and improve decision making.

 Analyze effectiveness of analytical products and services to constantly improve tools, procedures, and workflows that minimize risk and enhance customer experience.

 Ensures data and model governance is established to comply with internal audit requirements and ensures compliance with data governance and data privacy policies.

 Drive Analytics as a Product (AaaP)