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Director, Machine Learning Science (Remote)
Company | BioSpace |
Address | South San Francisco, CA, United States |
Employment type | FULL_TIME |
Salary | |
Category | Internet News |
Expires | 2023-07-25 |
Posted at | 10 months ago |
This position is open to remote work within the US or onsite work at our headquarters in South San Francisco. Our working hours are 9-5pm PT.
- Exemplify Freenome’s commitment to “servant leadership” by maximizing the full team’s potential for impactful contribution
- Work closely with ML research engineers to help them optimize infrastructure and better support the evaluation and development of new ML architectures
- Collaborate with computational biologists to better understand the nature of the underlying assays, molecular signals and biological systems, in order to maximize the predictive value of our training data
- Serve as a key thought leader on engineering and scientific leadership teams
- Possess an ability to explain complex modeling methodologies to staff in other disciplines, in order to increase their understanding of Freenome’s products and to identify new opportunities for application of ML techniques
- Create opportunities for team members to undertake independent work and shape their own professional directions
- Partner with other engineering and scientific leadership at Freenome to develop roadmaps and strategies
- Inspire a culture of innovation, translating discoveries into high-impact R&D pipelines and clinical applications
- Be capable of guiding the scientific work of your team and also digging into the code and data directly
- Be an expert in current statistical modeling and ML techniques — one who is able to rapidly evaluate and select methodologies that are appropriate for Freenome’s data types, and to develop new approaches when required
- Nurture and grow a high-functioning ML Science team, by mentoring existing staff and recruiting new staff with skill sets and development goals aligned with Freenome’s mission
- Lead a team that develops robust and performant classifiers for early cancer detection, based on multiomics molecular signals in the blood
- Track record of selflessly supporting highly effective cross-functional teams, and of collaborating closely with subject matter experts in other disciplines
- One or more common ML model development frameworks (e.g., PyTorch, TensorFlow, or scikit-learn).
- Semantic modeling and graph database techniques.
- Source code management (e.g., Git)
- Expertise with large-scale ML model development frameworks
- 3+ years leading or managing scientific and software engineering staff
- Outstanding command of modern ML model development software engineering and data architecture practices, including several of the following:
- Distributed high-performance computing, including workflow orchestration systems
- Containerization or other mechanisms for compute environment management.
- 7+ years post-PhD (or post-PhD-equivalent) experience with statistical and ML model development
- PhD in computer science, statistics, mathematics or other relevant scientific discipline. Alternatively, directly relevant professional experience and documented scientific achievements equivalent to a PhD in these disciplines
- Experience developing ML models in Python
- Experience applying ML techniques to high-dimensional molecular biology data types
- Experience with cloud-based high-performance computing
- Industry experience working in a diagnostics, pharmaceutical, or other biotechnology environment
- Family & Medical Leave Act (FMLA)
- Equal Employment Opportunity (EEO)
- Employee Polygraph Protection Act (EPPA)
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