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Tier-1 brand plus attractive ML role increases applicant density but seniority and specialization moderate it.
Core ML skills transfer across industries, but healthcare/people-analytics domain knowledge increases sensitivity moderately.
Explicit 10+ years, mandatory ML frameworks, productionization and leadership requirements make filters strict.
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Own end-to-end data science strategy and delivery for prioritized People Analytics and Insights (PAI) use cases focused on healthcare initiatives (e.g., leakage, adjudication accuracy, pricing issues).
Lead modeling strategy including problem framing, advanced model development (classification, anomaly detection, cost forecasting, uplift, time series), and ensure measurable business impact in Databricks and Snowflake environments.
Mentor senior/DS team members, collaborate with AI/ML Engineering for model productionization, and establish standards for quality, governance, and explainability.
Bachelor's degree in Computer Science, Data Science, Statistics, or related field.
10+ years of experience in Data Science, AI/ML, or similar role with proven track record of owning and leading AI/ML projects.
Proficiency with machine learning frameworks including Scikit Learn, TensorFlow, PyTorch; and strong programming skills in Python and SQL.
Experience managing full AI/ML project lifecycles including EDA, model development, tuning, monitoring; and using code version control (GitHub).
Experienced leader capable of defining and executing data science strategies in complex healthcare analytics contexts with measurable impact.
Strong technical expertise in diverse modeling techniques and frameworks, capable of mentoring and collaborating cross-functionally including production deployment.
Familiar with cloud platforms and modern data tools (preferred: Azure, Databricks, Snowflake) and skilled at applying ML Ops and workflow orchestration for scalable solutions.