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About This Role
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:
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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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\#LI\-MULTIPLE
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Unum and Colonial Life are part of Unum Group, a Fortune 500 company and leading provider of employee benefits to companies worldwide. Headquartered in Chattanooga, TN, with international offices in Ireland, Poland and the UK, Unum also has significant operations in Portland, ME, and Baton Rouge, LA \- plus over 35 US field offices. Colonial Life is headquartered in Columbia, SC, with over 40 field offices nationwide.
Unum is an equal opportunity employer, considering all qualified applicants and employees for hiring, placement, and advancement, without regard to a person's race, color, religion, national origin, age, genetic information, military status, gender, sexual orientation, gender identity or expression, disability, or protected veteran status.
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
Salary Context
This $73K-$150K range is in the lower quartile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Unum, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($111K) sits 42% below the category median. Disclosed range: $73K to $150K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Unum AI Hiring
Unum has 1 open AI role right now. They're hiring across Data Scientist. Based in Chattanooga, TN, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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