Data Scientist

Responsibilities

• Develop predictive models and analytic solutions, with moderate supervision, to support underwriting, pricing, and marketing functions within the Company.
• Collaborate in brainstorming potential data sources for predictive variables, identifying, acquiring, evaluating, and documenting data from internal and external sources.
• Extract and manipulate data using data management tools from various sources, understanding and combining data to create analytics datasets.
• Analyze data, draw meaningful conclusions, and contribute to developing solutions to enhance profitability and/or growth.
• Introduce innovative methodologies, algorithms, tools, and technologies to address assigned problems.
• Communicate findings to business partners, ensuring successful integration of projects into business processes.
• Participate in developing solutions to implement models into production, collaborating with IT in model design and testing.
• Support business requests requiring statistical analysis and collaborate with other analytics teams to achieve objectives.
• Build partnerships with key counterparts and monitor the performance and usage of models, ensuring reports meet audience needs.

Qualifications

• Minimum of a bachelor's degree, preferably in Statistics, Mathematics, Analytics, or Computer Science. Advanced degree in Statistics or Predictive Analytics is strongly preferred.
• 3+ years of predictive analytics experience, preferably in the Insurance industry or Financial Services.
• Proficiency in Python, including developing complex functions and classes within an automated framework.
• Data engineering skills, including visualization (histogram, heat map, interaction plots), data cleaning, and SQLDW schema creation.
• Modeling expertise in XG Boost, logistic regression, linear regression (severity and frequency modeling), model scoring, and interpretation.
• Automation skills, building functions and sklearn pipelines, comfortable with yaml files for configuring inputs.
• Familiarity with tools such as Databricks, VS Code, Github, and SQL server.
• Intermediate knowledge in statistical analysis and multivariate procedures; knowledge of Machine Learning techniques and AI is a plus.
• Experience with a statistical package (Python, R), data management (SQL), and/or programming is required; experience with Databricks is a plus.
• Experience with text analytics is a plus.

Proficient in Microsoft Suite applications

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