Senior Computational Biologist I

Why join Freenome?

Freenome is a high-growth biotech company developing tests to detect cancer using a standard blood draw. To do this, Freenome uses a multiomics platform that combines tumor and non-tumor signals with machine learning to find cancer in its earliest, most-treatable stages. 

Cancer is relentless. This is why Freenome is building the clinical, economic, and operational evidence to drive cancer screening and save lives. Our first screening test is for colorectal cancer (CRC) and advanced adenomas, and it’s just the beginning. 

Founded in 2014, Freenome has ~400 employees and continues to grow to match the scope of our ambitions to provide access to better screening and earlier cancer detection.  

At Freenome, we aim to impact patients by empowering everyone to prevent, detect, and treat their disease. This, together with our high-performing culture of respect and cross-collaboration, is what motivates us to make every day count.

Become a Freenomer

Do you have what it takes to be a Freenomer? A “Freenomer” is a determined, mission-driven, results-oriented employee fueled by the opportunity to change the landscape of cancer and make a positive impact on patients’ lives. Freenomers bring their diverse experience, expertise, and personal perspective to solve problems and push to achieve what’s possible, one breakthrough at a time.

About this opportunity:

At Freenome, we are seeing a Senior Computational Biologist I to help grow Freenome’s Research and Development team. As part of this team, you will apply your scientific expertise to the development of early, noninvasive tests for cancer detection. With a strong background in molecular biology, genomics, and proteomics, you will perform computational analyses enabling us to understand and identify cancer’s unique molecular signatures. You will work with a team of computational scientists to devise and develop best-in-class multiomic classifiers for the early detection of cancer. You will work in close collaboration with wet lab scientists and other computational scientists to both iterate upon and build new assays with the goal of developing clinical products that save lives.

The role reports to the Director, Computational Biology. This role can be a Hybrid role based in our Brisbane, California headquarters, or remote. 

What you’ll do:

• Lead the analysis and interpretation of molecular and clinical data in the context of early cancer detection, serving as a key thought leader on the Computational Science team
• Suggest research hypotheses and areas for potential computational model and assay improvement; then plan, scope, and execute associated research in partnership with a multidisciplinary team
• Effectively apply machine learning and statistical modeling tools, or develop novel ones, as appropriate for Freenome’s research questions and product requirements
• Develop classification models using novel molecular signatures and evaluate feasibility of translating these biomarkers to the product development phase
• In collaboration with wet lab scientists, rapidly characterize and iterate on experimental methods by providing real-time assessments of assay performance, quality control, and clinical/diagnostic utility
• Remain at the forefront of molecular techniques in oncology, and collaborate with the larger team to bring forward the next generation of assays for early cancer detection
  

Must haves:

• PhD in statistics, computational biology, cancer biology, or related quantitative field, with additional 3+ years postdoc or industry experience
• A solid foundation in cancer and molecular biology
• Solid quantitative reasoning and statistical analysis skills
• Strong computational and programming skills, ideally with Python expertise, and experience working with large datasets
• A passion for creating robust and reproducible computational analyses; fluency with software version control (Git)
• Deep understanding of cancer molecular biology
• Experience with high throughput NGS-based screening
• Excellent oral and written communication skills to communicate to both scientific and broader audiences
• Ability to work on a cross-functional team in our highly collaborative environment, working with both computational and experimental scientists 
  

Nice to haves:

•  Experience in building bioinformatics pipelines and computational tools to enable reproducible research analysis
• Experience in developing early cancer detection computational models using novel molecular/immunological signatures
• Domain expertise in molecular techniques in oncology and immunology for the early detection of disease
• Experience with immuno-assays technology
• Experience with proteomics
• Experience applying modern machine learning methods to analysis of biological systems
• Experience with blood-based clinical diagnostics 
  

Benefits and additional information:

The US target range of our base salary for new hires is $161,925 - $247,000. You will also be eligible to receive pre-IPO equity, cash bonuses, and a full range of medical, financial, and other benefits depending on the position offered.  Please note that individual total compensation for this position will be determined at the Company’s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education. We invite you to check out our career page @ freenome.com/job-openings/ for additional company information.  

Freenome is proud to be an equal-opportunity employer, and we value diversity. Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

Applicants have rights under Federal Employment Laws.  

• Family & Medical Leave Act (FMLA)
• Equal Employment Opportunity (EEO)
• Employee Polygraph Protection Act (EPPA)

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