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Strong employer brand, metro location, mid-level generalist ML role with broad skillset increases candidate competition.
Core ML, forecasting, and MLOps skills transfer across industries, though biotech domain knowledge adds some specificity.
Explicit 4-7 years requirement plus mandatory ML, MLOps, and technical skills drive strict shortlisting.
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Develop and deploy advanced machine learning, statistical, Bayesian, and causal models to improve forecasting and decision-making.
Manage the end-to-end model lifecycle including exploratory data analysis, feature engineering, model development, validation, deployment, monitoring, and explainability.
Collaborate with global cross-functional teams to build AI-driven automation, intelligent dashboards, and scenario-analysis tools that support business planning and operations.
Master’s or Bachelor’s degree in computer science, statistics, or STEM field.
4 to 7 years of Information Systems experience with strong data science exposure.
Proficient in Python and SQL for data analysis, statistical modeling, and automation.
Experience with machine learning model lifecycle, forecasting models, and handling large complex datasets.
Experienced with MLOps practices including model deployment, versioning, monitoring, and retraining in a production environment.
Proven ability to translate complex business problems into technical specifications and AI-driven analytical solutions.
Comfortable working in global, cross-functional teams and communicating complex insights to both technical and non-technical stakeholders.