Data Scientist

• Drive the development, deployment, and industrialization of enterprise applications using machine learning techniques such as classification, regression, and forecasting to generate value from structured and unstructured data for Commercial and Medical organizations.
• Help stakeholders define clear and impactful business priorities and, where possible, use subject matter expertise or existing analysis and research to influence and guide decision-making.
• Collaborate with Data Science Product Owners/Managers, Data Leads, ML Engineers, and other cross-functional teams to develop efficient machine learning-based applications, gain alignment, and deliver impactful business insights.
• Demonstrate a strong commitment to data ethics, model validation standards, data quality, governance, and regulatory compliance.

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 requirements., * Bachelor’s degree in Statistics, Mathematics, Computer Science, or a related quantitative field.

• Minimum of 5 years of experience in Data Science or related roles.
• Proficiency in programming languages such as Python and R.
• Knowledge of SQL for database management.
• Strong expertise in Machine Learning and Deep Learning techniques.
• Demonstrated experience developing end-to-end ML solutions, from conceptualization and prototype development through production deployment and monitoring.
• Experience with Data Science and cloud computing tools and platforms such as AWS and Google Cloud Platform.
• 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 and 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 aligned 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 or pharmaceutical industry, with a demonstrated commitment to strict data governance, regulatory compliance, and high-quality model validation standards.
• Experience with Deep Learning frameworks such as TensorFlow and PyTorch.
• Contributions to open-source projects or publications in Data Science.
• Relevant certifications in Data Science, Machine Learning, or AI technologies, such as Certified Analytics Professional, AWS, or similar certifications.
• Experience working with large and complex datasets using Hadoop, Spark, or other big data platforms.
• Proficiency in applying Machine Learning in various contexts, including insight generation, ROI calculation, text classification, clustering, and predictive modeling.
• Experience with data visualization tools such as Tableau, Qlik, Data Studio, or similar platforms.
• Experience translating research or analysis into concise and compelling business stories through presentations and written communication that influence decisions and strategy.