Senior Data Scientist

The Senior Data Scientist in Data, Analytics, and AI develops, deploys, and industrializes enterprise applications leveraging machine learning techniques to extract value from structured and unstructured data across Commercial and Medical domains. Acting as a strategic thought partner, the role involves shaping impactful business priorities, collaborating cross-functionally, and delivering actionable insights. The position requires a strong execution mindset, commitment to data quality and governance, and the ability to communicate complex findings to different audiences all while staying at the forefront of AI innovation and aligning with Genentech s standards and compliance., Drive the development, deployment, and industrialization of enterprise applications using machine learning techniques (e.g., classification, regression, and forecasting) to generate value from structured and unstructured data for Commercial and Medical organizations. Help stakeholders define clear, impactful business priorities, and where possible, use their own expertise or existing analysis/research to influence and guide. Collaborate with data science product owners/managers, data leads, ML Engineers, and other teams to develop efficient machine learning-based applications, gain alignment, and deliver impactful business insights. Strong commitment to data ethics, model validation standards, and regulatory compliance.

Data Science, Data Quality, Data Science, Enterprise Applications, Machine Learning, Unstructured Data, Artificial Intelligence (AI), Cross-Functional, Communication Skills, Data Analysis, Bachelor’s degree in Statistics, Mathematics, Computer Science, or a related quantitative field. Minimum 5 years of experience in data science or related roles. Proficiency in programming languages such as Python, R. Knowledge of SQL for database management. Strong expertise in Machine Learning and Deep Learning techniques Demonstrated experience developing the end-to-end ML solutions, from conceptualization and prototype development through to production deployment and monitoring. Experience with other Data Science and cloud-computing tools and platforms (AWS, GCP, etc.). Excellent verbal and written communication skills, with the ability to present complex data analyses to non-technical stakeholders. Proven track record of collaborating within cross-functional teams, partnering directly with data science product owners, ML engineers, and MLOps teams to deploy efficient, production-ready machine learning applications. Proven ability to translate ambiguous business challenges into clear, data-driven analytical initiatives that align with organizational objectives. Strong understanding of MLOps best practices, including CI/CD pipelines, model versioning, and performance monitoring to ensure scalability and reliability in production environments. Strong critical thinking and problem-solving abilities, with a detail-oriented approach to data analysis., Experience applying advanced data science and predictive modeling techniques within the healthcare/pharmaceutical industry, with a demonstrated commitment to strict data governance, regulatory compliance, and high-quality model validation standards Experience with deep learning frameworks (e.g., TensorFlow, PyTorch). Contributions to open source projects or publications in data science. Relevant certifications in data science, machine learning, or AI technologies (e.g., Certified Analytics Professional, AWS or similar certifications). Experience working with large, complex data using Hadoop or Spark or any other big data platforms. Proficiency using ML in a variety of contexts such as insight generation, ROI calculation, text classification, clustering etc. Experience with data visualization tools such as tableau, and/or Qlik, and/or data studio etc. Experience translating research or analysis to communicate (in presentations and in writing) concise and compelling business stories that influence decisions and strategy.