Senior Machine Learning Scientist, Healthcare Data (Remote)

The Senior Machine Learning Scientist at Freenome will lead the scientific direction and execution for the development of early, noninvasive detection tests for multiple cancers. They will utilize their expertise in machine learning, mathematics, statistics, and computer science to incorporate biology in the pursuit of early disease detection. Collaborating with computational biologists, molecular biologists, and engineers, they will drive research experiments and contribute to Freenome's mission of solving cancer. This role requires a PhD or equivalent research experience in a relevant field, along with demonstrated expertise in applied machine learning and strong knowledge of mathematical fundamentals. Proficiency in programming languages and familiarity with Linux server-based environments are also necessary.

• 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

• PhD or equivalent research experience in a relevant, quantitative field such as computer science (AI or ML emphasis), statistics, applied math, engineering, or a related field.
• 4+ years of post-PhD or industry experience working on the technical subject matter.
• Expertise, demonstrated by research publications or industrial 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.

• Base salary range of $157,250 - $240,000
• Pre-IPO equity
• Cash bonuses
• Full range of medical, financial, and other benefits depending on the position offered