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Mid-level, popular Data Scientist title, metro location, and strong employer brand increase applicant competition.
Core data science and MLOps skills are transferable, though pharma/regulatory experience is preferred.
Explicit 2–5 years plus mandatory MLOps, data pipeline, and LLM/tooling experience raises filter strictness.
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Own end-to-end development and maintenance of scalable, multi-brand, multi-channel data and analytics pipelines for time series forecasting and AI solutions.
Lead the design and delivery of predictive, prescriptive, and generative AI models leveraging large structured and unstructured datasets, including clinical and real-world data.
Drive adoption of AI-enabled scalable operating models by integrating modern data platforms, MLOps practices, and emerging AI tools while mentoring 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.
Proven expertise in building and maintaining ETL/ELT pipelines and use of MLOps tools such as MLflow, Git, CI/CD, containerization.
Experience with Python-based data science projects, data orchestration frameworks (Dagster, Airflow), and cloud platforms (AWS/Azure/GCP).
Experienced in handling large-scale healthcare, clinical trials, or regulated pharma datasets and delivering measurable business impact through predictive/generative AI models.
Comfortable working independently in ambiguous and evolving business environments, bridging engineering, analytics, and business domains.
Proactive adopter of emerging AI/ML technologies, with strategic focus on building reusable, scalable analytical workflows and improving workflow reliability, maintainability, and observability.