Decision Scientist

Job Summary

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.

Essential Tasks & Leadership Philosophy

• 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 analysis, Correlation analysis, Outlier detection, Multivariate analysis, Time-series analysis, Machine 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.

First Year Success Factors

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.

Education & Career Experience Requirements

Candidates would ideally reflect the following professional qualifications and experiences:

• High School Diploma or equivalent required. Bachelor's 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.