Data Scientist - LLM, Agentic AI & Predictive Modeling

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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

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Identify, design, and implement AI use cases leveraging LLMs, Agentic AI, generative AI, predictive modeling, machine learning, deep learning, and advanced analytics.

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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.

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Design and deploy predictive models including:

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Classification, regression, and ranking models

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Time series forecasting

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Anomaly and fraud detection

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Churn, propensity, and risk models

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Recommender systems and uplift modeling

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Translate predictive model outputs into actionable business signals, integrating them into downstream systems, dashboards, and AI-driven workflows.

Structured & Unstructured Data Modeling

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Engineer, train, and validate machine learning and deep learning models in Python for both structured and unstructured data, including tabular, text, and image data.

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Apply feature engineering, model calibration, interpretability, and performance optimization techniques to predictive models.

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Combine predictive models with LLMs and agentic systems (e.g., predictive scoring feeding agent decisions or RAG pipelines).

NLP, Multimodal & Computer Vision

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Apply NLP techniques such as text mining, semantic search, sentiment analysis, embeddings, and knowledge graph construction.

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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

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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.

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Collaborate cross-functionally with data engineering, product, and business teams to ensure solutions meet operational and strategic goals.

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Deliver clear insights, recommendations, and technical guidance to support enterprise AI adoption.

MLOps, Cloud & Responsible AI

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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.

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Knowledge of MLOps, cloud platforms, and Responsible AI , including CI/CD, model lifecycle management, Docker/Kubernetes deployment, and enterprise governance across Azure/AWS/GCP .

Use your skills to make an impact

Required Qualifications

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Bachelor's degree in Data Science, Computer Science, Statistics, Engineering, Mathematics , or related quantitative field.

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4+ years of hands-on experience in data science, machine learning, or advanced analytics

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Experience with language model fine-tuning

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Experience working with structured and unstructured data , including feature engineering and model development

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Experience building and deploying predictive models to support business decision-making

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Experience applying statistics, modeling, and analytics to translate complex data into insights, reports, and presentations

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Familiarity with LLMs, NLP, or generative AI and their application to enterprise use cases

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Working knowledge of MLOps, cloud platforms, and production deployment practices

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Ability to operate independently, make sound technical decisions in ambiguous situations, and collaborate across teams

Preferred Qualifications

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Master's degree

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Healthcare experience

Additional Information

You will report to a Lead Data Scientist

Location & Work Style

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This role is open to a remote work style in the US

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Eastern or Central time zone is preferred

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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 of Home or Hybrid Home/Office associates must meet the following criteria:

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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

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Satellite, cellular and microwave connection can be used only if approved by leadership

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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.

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Humana will provide Home or Hybrid Home/Office associates with telephone equ