Senior Associate Decision Scientist

Job Summary

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 leaders and become a trusted advisor for data-driven decision-making across Direct & Strategic Partnerships.
• Deliver actionable insights through advanced analytics, statistical modeling, and predictive techniques that solve complex business challenges and influence strategic outcomes.
• Lead high-impact analytical projects from problem definition through implementation, driving measurable improvements in growth, customer experience, operational performance, and profitability.
• Develop, validate, and deploy analytical models that identify key business drivers, improve forecasting capabilities, and support informed decision-making.
• Leverage Python, SQL, Power BI, and other analytical tools to transform complex datasets into scalable analytical solutions and executive-ready insights.
• Translate complex analytical findings into clear, actionable recommendations that influence leadership decisions and advance business objectives.

Education & Career Experience Requirements

Candidates would ideally reflect the following professional qualifications and experiences:

• Bachelor's degree in Data Science, Statistics, Mathematics, Economics, Business Analytics, Computer Science, or a related quantitative discipline. Equivalent combination of education and experience may be considered.
• Three or more years of experience applying advanced analytics, statistical modeling, predictive analytics, or quantitative problem-solving to support business decision-making. Experience in insurance, financial services, or other data-driven industries is preferred.
• Demonstrated ability to translate complex or ambiguous business questions into analytical approaches, actionable insights, and measurable business outcomes.
• Strong technical proficiency with Python, SQL, Excel, Power BI, and other analytical tools used for data exploration, statistical analysis, modeling, and visualization.
• Experience developing, validating, and deploying analytical models, including forecasting, segmentation, driver analysis, optimization, predictive modeling, and performance measurement.
• Solid foundation in statistical methods and analytical techniques, including hypothesis testing, regression analysis, experimental design, forecasting, and trend analysis.
• Proven ability to communicate complex analytical findings to both technical and non-technical audiences, translating data into clear recommendations that influence business strategy and decision-making.