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High competition due to Tier-1 brand, mid-level ML role, metro location, and broad LLM/ML requirements.
Medium because core ML/LLM skills transfer, but healthcare and clinical data experience is preferred.
High because of explicit 3+ years, mandatory LLM/Databricks/vector DBs, and production deployment experience.
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Design, develop, and deploy advanced data science and AI solutions including machine learning models and large language model (LLM) applications such as AI assistants and Retrieval-Augmented Generation (RAG).
Work with large-scale structured and unstructured healthcare-related datasets including clinical data and real-world evidence to build predictive models and generate actionable insights.
Partner with data engineering teams to build scalable data pipelines and production-ready AI systems, and communicate findings clearly to diverse stakeholders.
Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or related field.
Minimum 3 years of experience in data science, machine learning, or applied AI roles with proven production deployment of ML or LLM systems.
Strong proficiency in Python programming; experience with modern data platforms (Databricks, Spark, MLflow, Delta Lake).
Experience working with large-scale structured and unstructured datasets, especially in healthcare or life sciences, is highly desirable but not strictly mandatory.
Experience in building and deploying LLM-based AI applications including embedding pipelines, vector databases, and multi-step AI workflows with agentic frameworks.
Familiarity with healthcare or biopharma domains, including clinical data and real-world evidence sources, to support AI applications in these areas.
Ability to implement LLMOps practices focusing on model deployment, monitoring, prompt engineering, and quality evaluation in production environments.