Data Scientist

Title: Data Scientist II Location: Hybrid at South San Francisco, CA 94080 Duration: 4 Months Contract (Possibility of Extension) Client: PharmaceuticalDuties: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 stakeholdersRequired 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 environmentsPreferred 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