Senior Machine Learning Scientist (Remote)
Job Description
At Freenome, we are seeking a Senior Machine Learning Scientist to help grow the Freenome Computational Science team. The ideal candidate will have a strong foundation in Machine Learning, Mathematics, Statistics and Computer Science to incorporate biology in the pursuit of early detection of disease. You will be responsible for leading the scientific direction and execution for the development of early, noninvasive detection tests for multiple cancers. You will also work with computational biologists, molecular biologists and engineers to drive the iteration of research experiments and become the primary drivers towards Freenome’s mission of solving cancer.
You are passionate about innovation and demonstrated initiative in tackling new areas of research, and you will have a significant impact on the continued growth of a high profile technology organization that is changing the landscape on early cancer detection.
Responsibilities
- Lead the direction and development of cutting edge research in statistical modeling and inference of biological problems (including cancer research, genomics, computational biology/bioinformatics, immunology, therapeutics, and more)
- Lead research projects that propose new methods and perspectives for modeling various biological changes resulting from diseases such as cancer, autoimmune disease, and infection
- Build and immediately apply core analyses in support of a long term research program in data driven biology
- Interface with product teams to identify potential new problem areas in need of an ML solution
- Take a mindful, transparent, and humane approach to your work
Job Requirements
- PhD or equivalent research experience in a relevant, quantitative field such as Computer Science (AI or ML emphasis), Statistics, Mathematics, Engineering, or a related field
- 3+ years of post-PhD or industry experience working on the technical subject matter
- Expertise, demonstrated by research publications or industry experience, in applied machine learning, data mining, pattern recognition, or AI
- Strong knowledge of mathematical fundamentals: statistics, probability theory, linear algebra
- Practical and theoretical understanding of fundamental models and algorithms in supervised and unsupervised learning: generalized linear models, kernel machines, decision trees, neural networks; boosting and model aggregation; clustering and mixture modeling; Bayesian inference and model selection, EM, variational inference, Gaussian processes, causal inference, Monte Carlo methods; dimensionality reduction and manifold learning
- Proficiency in a general-purpose programming language: Python, Java, C, C++, etc
- Familiarity working in a Linux server-based environment
- Excellent ability to clearly communicate across disciplines and work collaboratively towards next steps in experimental iterations
Nice to haves:
- Deep domain-specific experience in computational biology, genomics or a related field
- Experience in scientific parallel computing like an HPE systems, and/or in distributed computing environments like Kubernetes
- Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems
- Experience in high-performance computing, including SIMD or GPU performance optimization
