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Tier-1 brand, mid-level ML role, metro location, and broad data science skillset drive high competition.
Core ML and data engineering skills are broadly transferable, though life-sciences preference raises sensitivity to medium.
Extensive mandatory ML, cloud, big-data, and education/experience requirements indicate high shortlisting strictness.
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Develop and deploy advanced data science, machine learning, and AI solutions to solve complex business challenges across the enterprise.
Drive operational efficiencies and enhance decision-making capabilities through sophisticated analytics.
Work with advanced technologies in a collaborative environment to contribute to healthcare and scientific innovation.
Advanced Degree plus 3 years or Bachelor's Degree plus 5 years of experience in data science and machine learning.
Proficiency in Python, R, SQL, and machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, including LLM and Agentic AI.
Experience with cloud platforms (AWS, Azure), big data technologies (Spark, Databricks), data visualization tools (Power BI, Tableau), ETL processes, and DevOps practices (Git).
Preferred educational background in Data Science, Computer Science, Statistics, Mathematics or related quantitative fields.
Experienced in translating complex business problems into analytical solutions within healthcare, life sciences, or clinical research domains.
Capable of working both independently and collaboratively in diverse teams to deliver data-driven insights.
Skilled in managing projects and communicating technical concepts effectively to non-technical stakeholders.