General Summary:We're looking for a mid-level Data Scientist who can bridge the gap between our most important workforce and talent opportunities and what is possible with today's AI, machine learning, and advanced analytics capabilities.
This highly visible role sits at the intersection of applied AI, data science, scalable data products, and people analytics. You will partner with HRBPs, Talent, Operations, IT, Legal, and data leaders to identify high-value opportunities, design practical solutions, build working prototypes, and help move validated ideas into production.
This is not a purely research-oriented data science role. We're looking for someone who can help translate ambiguous talent and workforce challenges into clear problem statements, build tangible AI-enabled solutions that stakeholders can see and test, and partner across teams to ensure those solutions are responsibly deployed, adopted, and measured.
You'll work with other data scientists and data engineers to build intelligent systems - not just models - using modern AI approaches such as LLMs, embeddings, RAG, agentic workflows, workflow automation, and predictive modeling. You'll help shape the organization's AI roadmap for workforce and talent analytics while ensuring solutions are practical, scalable, secure, ethical, and aligned to business value.
This role is ideal for someone who thrives in ambiguity, moves quickly from concept to prototype, exercises strong judgment about what is worth building, and can influence senior stakeholders through insight, technical credibility, and delivered outcomes.
Preferrable experience within HR/People Analytics domain.
Job Specifications- Bachelor's degree in quantitative field is required, Master's is preferred
- 4+ years of professional experience or equivalent relevant work experience preferred
- Core Data Science Capabilities: Deep expertise in at least two of the following skillsets preferred; and competency in the other:
- Programming & Process automation: Experience with file I/O, database integrations, and APIs to build automated analytics pipelines. Understanding of process research and design, which may be demonstrated through use of DevOps, automation, data mining, web scraping, or object-oriented software is preferred.
- Data Visualization: Expertise on at least one visualization tool with working knowledge of others and expertise in static data visualization. Understanding of dynamic data visualization.
- Statistics & Statistical modeling: Expertise using statistical inference and regression. Solid understanding of machine learning algorithms. Solid understanding of feature selection and extraction. Conducts end-to-end machine learning tasks from problem synthesis to model deployment.
- Data Extraction, Transformation, and Loading: Preferred skills include: Expertise in writing complex SQL queries that join multiple tables/databases. Independently explore databases/tables to identify best data sources to solve business problems. Demonstrates ability to troubleshoot complex SQL queries with little guidance. Demonstrates ability to create logical data models by combining data from multiple sources including internal and external data.
- Core business capabilities: Demonstrated communication skills, experience in financial services, leadership experience working with senior management and executive leadership, and attention to detail while effectively and independently prioritizing work and managing multiple projects simultaneously.
- Leadership Capabilities: Demonstrated ability to coach or mentor team members, ability to commit quickly and positively to change. Viewed as a promoter of change management and leads proof of concept work and prototyping when necessary
- Preferred characteristics: Entrepreneurial self-starter, a thorough, results-oriented problem-solver, and a lifelong learner with voracious curiosity AND intermediate understanding of their organization
Principal Duties and Responsibilities- Design, develop, and deploy AI/ML solutions-including LLM-powered applications-that solve complex workforce and organizational challenges
- Translate ambiguous business and HR questions into scalable data products, models, and decision-support tools
- Build and maintain end-to-end data science workflows, from data extraction (e.g., enterprise data warehouses) to model deployment and monitoring
- Partner with HRBPs, talent leaders, and executives to deliver actionable insights on topics such as internal mobility, skills, performance, and workforce planning
- Develop and productionize advanced analytics solutions using modern AI frameworks (e.g., LLMs, embeddings, RAG architectures)
- Create reusable data assets, semantic layers, and metadata frameworks to improve analytics scalability and self-service
- Collaborate with data engineering teams to optimize data pipelines, ensure data quality, and enable near real-time analytics
- Communicate complex analytical findings and AI concepts clearly to non-technical stakeholders, influencing strategic decision-making
- Ensure responsible AI practices, including data privacy, bias mitigation, and ethical use of employee data
- Stay current on emerging AI trends and proactively identify opportunities to incorporate new technologies into the organization
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The base salary range for applicants for this position is listed below. Unless actual salary is indicated above in the job description, actual pay will be based on skill, geographical location and experience.
$73,300.00-$150,500.00
Additionally, Unum offers a portfolio of benefits and rewards that are competitive and comprehensive including healthcare benefits (health, vision, dental), insurance benefits (short & long-term disability), performance-based incentive plans, paid time off, and a 401(k) retirement plan with an employer match up to 5% and an additional 4.5% contribution whether you contribute to the plan or not. All benefits are subject to the terms and conditions of individual Plans.
Company:
Unum
