Back
Strong global brand, popular mid-level data scientist title, broad ML/LLM skillset increases applicant competition.
Core ML, MLOps, and data engineering skills are transferable, though pharma/regulatory experience increases specificity.
Explicit 2–5 years requirement, mandatory applied ML/MLOps skills, and specific tooling raise shortlist rigidity.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and deliver scalable data science solutions including multi-brand, multi-channel time series forecasting and advanced ML models with measurable business impact.
Own end-to-end development and maintenance of data and analytics pipelines ensuring reliability, maintainability, and observability using tools like Dagster, Databricks, and Domino.
Lead adoption of emerging AI/ML and agentic-system tools to build future-ready capabilities and mentor junior data scientists.
Bachelor’s, Master’s, or Ph.D. in Data Science, Computer Science, Statistics, Engineering, or related field.
2–5 years of experience in applied machine learning and problem-solving roles.
Experience building and maintaining robust ETL/ELT, transformation, and validation pipelines; strong hands-on Python expertise.
Proficiency with MLOps tools (MLflow, Git, CI/CD, containerization) and working with large structured/unstructured datasets using SQL, NoSQL, and document stores.
Experienced in deploying and scaling advanced machine learning and AI solutions including predictive, prescriptive, and generative models in commercial or regulated environments.
Capable of working independently in ambiguous, evolving problem spaces, integrating engineering discipline with innovative AI-enabled workflows.
Comfortable collaborating across engineering, analytics, and business teams and presenting complex technical information to diverse audiences.