Crinetics Pharmaceuticals is seeking to drive the adoption of artificial intelligence and machine learning across the organization to transform the lives of patients with endocrine diseases and endocrine-related tumors.
Requirements
- Expert proficiency in programming languages such as Python or R.
- Extensive experience with machine learning libraries and frameworks (e.g., Scikit-learn, TensorFlow, PyTorch).
- Strong knowledge of SQL and experience working with relational and non-relational databases.
- Hands-on experience with cloud computing platforms, preferably Microsoft Azure and Databricks.
- Solid understanding of software engineering best practices, including version control (Git), testing, and CI/CD.
- Experience with life sciences data sources (e.g., genomic data, clinical trial data, real-world evidence).
- Knowledge of GxP regulations and experience working in a regulated environment.
Responsibilities
- Strategic Development: Collaborate with the Executive Director of Enterprise Solutions & Innovation to define and execute the company's AI/ML roadmap.
- Solution Evaluation and Implementation: Lead the technical evaluation of both internal and external AI/ML solutions.
- Cross-Functional Collaboration: Partner with stakeholders from R&D, Clinical Operations, Regulatory Affairs, and other departments to understand their needs and translate them into data science questions and solutions.
- Data and Infrastructure: Work closely with IT and data engineering teams to ensure the availability and quality of data required for AI/ML initiatives.
- Innovation and Research: Stay abreast of the latest advancements in machine learning, artificial intelligence, and data science.
- Design, build, and deploy scalable and robust machine learning models on our Azure/Databricks platform.
- Assess and integrate vendor-provided AI/ML technologies, ensuring they meet our scientific and business requirements.
Other
- Education: Master's or Ph.D. in a quantitative field such as Computer Science, Data Science, Statistics, Computational Biology, or a related discipline.
- Experience: 8-10 years with 3-5 years of hands-on experience in data science and machine learning.
- Excellent problem-solving and analytical skills.
- Strong communication and interpersonal skills, with the ability to convey complex technical concepts to non-technical audiences.
- Travel: up to 5% of your time.
