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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
- 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
- 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 .
Use your skills to make an impact
Required 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
Additional Information
You will report to a Lead Data Scientist
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 of Home or Hybrid Home/Office associates 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 equ