Data Scientist

Role Description

The Data Scientist 2 – LLM, Agentic AI & Predictive Modeling is a hands-on role focused on designing, deploying, and scaling predictive models, LLM-based, and agentic AI solutions in enterprise environments. The role combines advanced analytics, machine learning, and generative AI with strong MLOps, cloud deployment, and Responsible AI practices to deliver production-ready solutions that drive measurable business and customer impact.

Key Responsibilities:

• LLM, Agentic AI & Predictive Modeling:
  • Identify, design, and implement AI use cases leveraging LLMs, Agentic AI, generative AI, predictive modeling, machine learning, deep learning, and advanced analytics.
  • Develop, fine-tune, and deploy LLM-based and agent-based systems for enterprise use cases such as conversational AI, workflow automation, reasoning systems, and decision support.
  • Design and deploy predictive models including:
    • Classification, regression, and ranking models
    • Time series forecasting
    • Anomaly and fraud detection
    • Churn, propensity, and risk models
    • Recommender systems and uplift modeling
  • Translate predictive model outputs into actionable business signals, integrating them into downstream systems, dashboards, and AI-driven workflows.
• Structured & Unstructured Data Modeling:
  • Engineer, train, and validate machine learning and deep learning models in Python for both structured and unstructured data, including tabular, text, and image data.
  • Apply feature engineering, model calibration, interpretability, and performance optimization techniques to predictive models.
  • Combine predictive models with LLMs and agentic systems (e.g., predictive scoring feeding agent decisions or RAG pipelines).
• NLP, Multimodal & Computer Vision:
  • Apply NLP techniques such as text mining, semantic search, sentiment analysis, embeddings, and knowledge graph construction.
  • Build and deploy computer vision and multimodal models, including image classification, object detection, semantic segmentation, and visual search using PyTorch, TensorFlow, Keras, and OpenCV.
• Delivery, Consulting & Collaboration:
  • Lead hands-on execution for rapid prototyping, MVP development, and scaled production delivery of identified opportunities for predictive analytics, LLMs, and agentic AI that deliver measurable business value.
  • Collaborate cross-functionally with data engineering, product, and business teams to ensure solutions meet operational and strategic goals.
  • Deliver clear insights, recommendations, and technical guidance to support enterprise AI adoption.
• MLOps, Cloud & Responsible AI:
  • Experience deploying and monitoring predictive, LLM, and deep learning models, including performance, drift, bias, explainability, and business impact, using advanced metrics and A/B testing.
  • Knowledge of MLOps, cloud platforms, and Responsible AI, including CI/CD, model lifecycle management, Docker/Kubernetes deployment, and enterprise governance across Azure/AWS/GCP.

Qualifications

• Bachelor's degree in Data Science, Computer Science, Statistics, Engineering, Mathematics, or related quantitative field.
• 4+ years of hands-on experience in data science, machine learning, or advanced analytics.
• Experience with language model fine-tuning.
• Experience working with structured and unstructured data, including feature engineering and model development.
• Experience building and deploying predictive models to support business decision-making.
• Experience applying statistics, modeling, and analytics to translate complex data into insights, reports, and presentations.
• Familiarity with LLMs, NLP, or generative AI and their application to enterprise use cases.
• Working knowledge of MLOps, cloud platforms, and production deployment practices.
• Ability to operate independently, make sound technical decisions in ambiguous situations, and collaborate across teams.

Preferred Qualifications

• Master's degree.
• Healthcare experience.

Location & Work Style

• This role is open to a remote work style in the US.
• Eastern or Central time zone is preferred.
• Ability to travel for on-site team meetings (occasionally) on East Coast.

Work at Home Guidance

• To ensure Home or Hybrid Home/Office associates' ability to work effectively, the self-provided internet service must meet the following criteria:
  • At minimum, a download speed of 25 Mbps and an upload speed of 10 Mbps is recommended; wireless, wired cable or DSL connection is suggested.
  • Satellite, cellular and microwave connection can be used only if approved by leadership.
• Associates who live and work from Home in the state of California, Illinois, Montana, or South Dakota will be provided a bi-weekly payment for their internet expense.
• Humana will provide Home or Hybrid Home/Office associates with telephone equipment appropriate to meet the business requirements for their position/job.
• Work from a dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information.

Scheduled Weekly Hours

• 40

Pay Range

• The compensation range below reflects a good faith estimate of starting base pay for full time (40 hours per week) employment at the time of posting. The pay range may be higher or lower based on geographic location and individual pay will vary based on demonstrated job related skills, knowledge, experience, education, certifications, etc.
• $97,900 - $133,500 per year.
• This job is eligible for a bonus incentive plan based upon company and/or individual performance.

Description of Benefits

• Humana, Inc. and its affiliated subsidiaries offer competitive benefits that support whole-person well-being, including:
  • Medical, dental and vision benefits.
  • 401(k) retirement savings plan.
  • Time off (including paid time off, company and personal holidays, volunteer time off, paid parental and caregiver leave).
  • Short-term and long-term disability.
  • Life insurance and many other opportunities.

Application Deadline

• 05-07-2026

Equal Opportunity Employer

It is the policy of Humana not to discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or protected veteran status.