Data Scientist - Statistician - Hybrid

When you join the team at Unum, you become part of an organization committed to helping you thrive.

Here, we work to provide the employee benefits and service solutions that enable employees at our client companies to thrive throughout life’s moments. And this starts with ensuring that every one of our team members enjoys opportunities to succeed both professionally and personally. To enable this, we provide: 

• Award-winning culture 

• Inclusion and diversity as a priority 

• Performance Based Incentive Plans

• Competitive benefits package that includes: Health, Vision, Dental, Short & Long-Term Disability 

• Generous PTO (including paid time to volunteer!) 

• Up to 9.5% 401(k) employer contribution 

• Mental health support 

• Career advancement opportunities 

• Student loan repayment options 

• Tuition reimbursement

• Flexible work environments 

*All the benefits listed above are subject to the terms of their individual Plans.

And that’s just the beginning…  

With 10,000 employees helping more than 39 million people worldwide, every role at Unum is meaningful and impacts the lives of our customers. Whether you’re directly supporting a growing family, or developing online tools to help navigate a difficult loss, customers are counting on the combined talents of our entire team. Help us help others, and join Team Unum today! 

General Summary:

We are seeking a highly skilled and motivated Data Scientist - Statistician to transform complex data into actionable insights that drive meaningful business outcomes.

This role requires expertise in programming, applied statistics, and data analysis, along with a growing understanding of business operations. The ideal candidate will independently identify, plan, and execute advanced statistical modeling and analyses while effectively communicating results to influence decision-making.

Location: 2 days per week at our Portland, ME, Chattanooga, TN, Columbia, SC or Atlanta, GA campus

Job Specifications:
 

Education:

• Master’s degree in Statistics, Applied Mathematics, or a related field required; Ph.D. preferred.
   

Experience:

• Professional experience in data science, applied statistics, or a related field. Equivalent relevant work experience may be considered.
   

Technical Skills:

• Programming: Proficiency in Python, R, or similar languages for data analysis and algorithm implementation.
• Statistical Modeling: Expertise in regression, statistical inference, machine learning algorithms, feature selection, and model deployment.
• Data Visualization: Experience in creating compelling visualizations (e.g., maps, charts, graphs) to present complex analyses.
• Data Integration: Advanced knowledge of ETL processes, including writing complex SQL queries and combining data from multiple sources (DB2, SQL Server, Web APIs, Teradata).
   

Business Acumen:

• Strong communication skills with experience presenting to senior leadership.
• Ability to manage multiple priorities and projects with attention to detail.
   

Leadership Skills:

• Experience mentoring team members and promoting change management welcome
• Entrepreneurial mindset, problem-solving, and ability to thrive in diverse team environments.
   

Key Responsibilities:
 

Data Collection & Analysis

• Plan and execute data collection strategies, including sampling techniques and determining appropriate sample sizes for projects.
• Develop and evaluate statistical models, including experimental design (DoE), residual analysis, and variance analysis.
• Analyze data to uncover actionable insights, identifying relationships, trends, and potential business impacts.
   

Statistical Modeling

• Select and apply advanced statistical methods, such as univariate/multivariate analysis, generalized linear models, time-series modeling, and Bayesian approaches.
• Construct and refine predictive models for understanding events, forecasting behaviors, and identifying risks through scoring or clustering.
   

Data Integration & Preparation

• Integrate data from multiple sources (e.g., DB2, SQL Server, Web APIs, Teradata) to create analysis-ready datasets.
• Employ validation, aggregation, and reconciliation techniques to ensure data quality and reliability.
   

Communication & Leadership

• Interpret and communicate analytical findi