Director, AI/ML Transformation

Job Posting End Date: April 10

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:

Unum Group seeks Directors, AI/ML Transformation in Chattanooga, TN.

Applicants who are interested in this position may apply at www.jobpostingtoday.com (Ref #18817) for consideration.

• Lead AI/ML Strategy and Roadmap – Define and oversee the strategic direction for AI/ML transformation initiatives, aligning model development and deployment with overall business goals.
• Design and Develop Advanced Models – Architect, train, and validate machine learning and generative AI models (including supervised, unsupervised, and reinforcement learning approaches) to solve complex business problems.
• Implement and Operationalize AI Solutions – Translate model outputs into production-ready digital products, ensuring scalability, robustness, and integration with existing enterprise systems.
• Model Governance and Risk Management – Establish and enforce responsible AI practices including explainability, fairness, bias detection, security, and regulatory compliance throughout the AI lifecycle.
• Cross-Functional Collaboration – Partner with product managers, engineers, data scientists, and business leaders to identify high-value use cases and drive adoption of AI/ML capabilities.
• Performance Monitoring and Continuous Improvement – Implement monitoring frameworks to track accuracy, drift, and performance of deployed models, ensuring timely retraining and updates.
• Team Leadership and Talent Development – Mentor and guide data science and engineering teams, fostering a culture of innovation, experimentation, and best practices in AI/ML.
• Stakeholder Communication – Present technical concepts and business impact of AI/ML initiatives to executive leadership, external partners, and non-technical audiences in a clear and compelling way.
• Innovation and Research – Stay current on emerging AI/ML methodologies, tools, and industry trends; pilot new technologies such as large language models and generative AI to maintain a competitive edge.

Requires a Bachelor’s degree in AI/ML, Data Science or related analytical field plus 5 years of related experience. Alternatively, employer will accept a Master’s degree in AI/ML, Data Science or related analytical field plus 3 years of related experience. Requires 5 years of experience with a Bachelor’s degree, or 3 years with a Master’s degree, with the following: Statistics, calculus, and algorithm-based mathematics; building generalized linear models, and processing/analyzing time series data; Coding abilities with an emphasis on Python and understanding many ML/AI based Python libraries, including Scikit-Learn, NumPy, pandas, and OpenCV; Deep-learning (neural network) model building, training, and deployment, with PyTorch or Tensorflow; Other forms of supervised learning, unsupervised learning, and optimization algorithms; experience with building gradient boosted tree models (LightGBM orXGBoost); DevOps and the ownership of a model’s lifecycle, including experimentation, deployment, monitoring of feature drift and inference performance, model evaluation, and model iteration; Feature engineering for model