Data Scientist

Title: Data Scientist II Location: Hybrid at South San Francisco, CA 94080 Duration: 4 Months Contract (Possibility of Extension) Client: Pharmaceutical Duties: • Model Development & Optimization • Lead the design, development, and optimization of machine learning models to improve targeting accuracy and business performance • Apply advanced Machine Learning and Deep Learning techniques to refine existing models and develop new solutions as needed • Feature Engineering & Data Preparation • Design and build scalable feature pipelines, transforming raw and complex datasets into high-quality model inputs • Work with large, messy datasets (e.g., claims data) while ensuring data integrity and usability • Production & Pipeline Scalability • Transition analytical models and scripts into robust, production-ready pipelines • Ensure code quality, documentation, and adherence to engineering and data science best practices • Support model deployment, monitoring, and ongoing performance improvements • Cross-Functional Collaboration & Insight Translation • Partner with data science, analytics, and business teams to translate business problems into technical solutions • Communicate model methodologies, assumptions, and results clearly to non-technical stakeholders Required Skills & Experience: • Master’s or PhD in Data Science, Computer Science, Statistics, or a related field • Minimum 5+ years of hands-on experience in data science and machine learning model development • Proven track record of taking models from ideation through production deployment • Strong proficiency in Python, SQL, and AWS • Deep expertise in Machine Learning and/or Deep Learning techniques • Experience working with large-scale, complex datasets in collaborative environments Preferred Skills & Experience: • Experience within healthcare, pharmaceutical, or other highly regulated industries • Hands-on experience with claims data or similarly complex, regulated datasets • Strong understanding of data privacy, compliance, and governance requirements • Experience with MLOps practices, including pipeline automation, deployment, and monitoring