Data Scientist

Roles & Responsibilities

• Build and deploy AI/ML models with general guidance.
• Act as a liaison between the ML Engineers and Data Science team, managing AI/ML model deployments and conducting testing.
• Participate in the development of modeling presentations and present results/topics to stakeholders (internal & external) and management as requested.
• Concentrate on loosely defined problems which require application of creative approaches.
• Contribute to projects that yield actionable insights the business can use to increase customer satisfaction, policy growth, retention, and profitability.
• Complete required data preparation, including data input with guidance.
• Identify data anomalies and seek guidance on necessary adjustments.
• Identify data and operational issues requiring specialized analytics attention, data scrubbing, and modeling techniques.
• Create and maintain modeling best practices and new techniques in coordination with internal and external business partners.
• Explain advanced analytic and modeling procedures in the language that audiences with no predictive analytics training can easily find connections to.
• Understand stakeholders’ requests and hidden issues and explain possible solutions in their language.
• Schedule and prioritize workload demands.
• Maintain very good organization skills.
• Maintain strong interpersonal skills - listen deeply, ask correct questions, take notes, complete professional oral and written business communications (including email communications), presentations, and build business relationships in order to better understand insurance company processes and functions.

Required Technical Skills

• Proficiency in statistical programming languages such as R, Python, SAS, or similar, alongside general programming and SQL knowledge.
• Demonstrated experience building and deploying statistical and machine learning models to address business challenges.
• Skilled in data wrangling, quality evaluation, transformation, and cleaning for analysis.
• Experience with AI/ML model testing, validation, deployment, and integration into CI/CD pipelines.
• Familiarity with tools such as MLflow for model experimentation and registration, Git for version control, and agile frameworks using tools like Azure DevOps.

Skills / Knowledge / Experience

• Develop and project manage modeling projects and statistical analyses.
• Effectively communicate results to business partners and help drive business decisions.
• Participate in creation and discussion of best practices and propose new techniques.
• Mentor junior team members.

Education, Certifications & Designations

• Master’s degree in Business Analytics, Statistics, Computer Science, or Statistics/Math and Economics double major, or Statistics/Math and Computer Science double major or Statistics/Math and Operation Research double major