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Popular mid-level ML role in metro at a strong global employer creates high applicant competition.
Applied ML and forecasting skills transfer across industries, though pharma/regulatory experience moderately favors fit.
Explicit 2–5 years plus mandatory forecasting and MLOps skills increase screening rigidity.
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Lead and own end-to-end time-series forecasting projects impacting commercial demand and planning decisions.
Develop, maintain, and scale advanced forecasting models using statistical, ML, and AI techniques in partnership with Finance, Commercial, Market Access, and Analytics stakeholders.
Influence strategic decisions through high-impact analytical solutions and provide mentorship to junior data scientists, ensuring robust MLOps practices.
Bachelor’s, Master’s, or Ph.D. in Data Science, Computer Science, Statistics, Engineering, or related field.
2-5 years experience in applied machine learning and problem-solving roles with demonstrable success in predictive or AI models.
Strong technical skills in Python data science workflows, time-series forecasting methods (ARIMA, Prophet, Holt-Winters), and MLOps tools like MLflow, Git, CI/CD, containerization.
Experience with large structured and unstructured datasets (SQL, NoSQL), and familiarity with cloud data ecosystems (AWS/Azure/GCP, Spark, Databricks, BigQuery, Snowflake).
Experienced in independently translating ambiguous business problems into scalable forecasting and ML/AI solutions with direct commercial impact.
Comfortable working autonomously in fast-paced, ambiguous environments and capable of leading cross-functional collaboration including finance and commercial teams.
Possesses strong statistical foundations and hands-on expertise in modern data platforms, with exposure to advanced AI including agentic AI architectures and autonomous analytical agents.