Decision Scientist

The Decision Scientist provides critical analytical support across the organization by gathering analyzing and interpreting data to solve business challenges and support strategic decision-making. This role leverages moderately advanced statistical mathematical and quantitative techniques to identify trends uncover insights and develop data-driven recommendations that improve operational efficiency drive growth and enhance profitability.

The ideal candidate is a strong analytical thinker who can work independently collaborate with business partners across functions and effectively communicate complex findings to technical and non-technical audiences. This role may also provide informal guidance and mentorship to less experienced team members.

• Apply moderately advanced statistical mathematical and analytical techniques to solve medium-to-large-scale business problems impacting current and future business strategies.
• Build descriptive and explanatory models or leverage outputs from predictive and machine learning models developed by others.
• Analyze large and complex datasets to identify trends patterns risks and business opportunities.
• Utilize data visualization tools and advanced analytical methods including:Distribution analysisCorrelation analysisOutlier detectionMultivariate analysisTime-series analysisMachine learning techniques.
• Conduct exploratory data analysis (EDA) test hypotheses and validate findings with business stakeholders.
• Partner with cross-functional teams to understand business objectives and develop data-driven solutions.
• Collect integrate and transform data from multiple sources into cohesive datasets for analysis.
• Evaluate data quality and apply data governance and hygiene practices to ensure reliability and accuracy.
• Perform quality-control checks document processes and collaborate with peers on data validation efforts.
• Work closely with IT Data Engineering Data Science and other technical teams to optimize data pipelines and resolve data quality issues.
• Support the deployment monitoring and maintenance of predictive models within business workflows.
• Translate complex analyses into clear actionable insights and recommendations for business leaders.
• Utilize effective storytelling and visualization techniques to make analytical findings accessible and impactful.
• Stay current on emerging data science methodologies technologies and industry best practices.
• Contribute to a culture of continuous learning and knowledge sharing.
• Provide informal coaching or mentorship to less experienced team members as needed.
• Perform other duties as assigned.

Within the first 12 months a successful candidate can expect to have achieved the following:

• Establish strong partnerships with business stakeholders and become a trusted analytics advisor for key initiatives.
• Deliver actionable insights through advanced analytics statistical modeling and data visualization that support business decisions.
• Lead medium-to-large analytical projects that drive measurable improvements in efficiency growth customer experience or profitability.
• Ensure data accuracy integrity and governance through rigorous validation documentation and quality control practices.
• Identify emerging trends and opportunities translating complex data into clear recommendations that influence leadership decisions.

Candidates would ideally reflect the following professional qualifications and experiences:

• High School Diploma or equivalent required. Bachelors degree preferred in data science statistics mathematics business analytics or similar. Equivalent work experience may be considered in place of a degree.
• Minimum of three years of work experience required in data analysis statistical or mathematical modeling or related. Experience in insurance industry preferred.
• Demonstrated experience translating ambiguous business questions into analytical frameworks actionable insights and data-driven recommendations.
• Proficiency with analytical and visualization tools such as SQL Python R SAS Tableau Power BI or similar technologies.
• Experience designing conducting and evaluating experiments forecasts scenario analyses or predictive models to influence business outcomes and strategic decision-making.
• Strong understanding of statistical methods hypothesis testing regression analysis segmentation forecasting and performance measurement techniques.
• Proven ability to communicate complex analytical findings to executive and non-technical audiences influencing decisions through clear storytelling and business acumen.
• Insurance financial services risk management or highly regulated industry experience preferred.