Staff Computational Biologist, Assay Research and Development

South San Francisco, CA

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Freenome

At Freenome, we’re developing next-generation blood tests for early cancer detection, powered by our multiomics platform. Learn more.

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Why join Freenome?

Freenome is a high-growth biotech company on a mission since 2014 to create tools that empower everyone to prevent, detect, and treat their disease. 

To achieve this mission, Freenome is developing next-generation blood tests to detect cancer in its earliest, most treatable stages using our multiomics platform and machine learning techniques. Our first blood test will detect early-stage colorectal cancer and advanced adenomas.

To fight the war on cancer, Freenome has raised more than $1.1B from leading investors including a16z, GV (formerly Google Ventures), T. Rowe Price, BainCapital, Perceptive Advisors, RA Capital Management, Roche, Kaiser Permanente Ventures, and the American Cancer Society’s BrightEdge Ventures. 

Are you ready for the fight? A ‘Freenomer’ is a mission-driven employee who is fueled by the opportunity to make a positive impact on patients' lives, who thrive in a culture of respect and cross collaboration, and whose work makes a significant impact on the company and their career. Freenomers are determined, patient-centric, and outcomes-driven. We build teams around divergent expertise, allowing us to solve problems and ascertain opportunities in unique ways. We are dedicated to advancing healthcare, one breakthrough at a time.

 

About this opportunity:

As a Staff Computational Biologist on the Computational Biology, Assay Research and Development team at Freenome, you will help build novel NGS and immunological assays for early, noninvasive tests for cancer detection. You will bring a strong foundation in molecular biology and genomics to perform computational analyses enabling us to understand and identify cancer’s unique molecular signatures. 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. This role will report to the Manager and Staff Computational Biologist.

What you’ll do:

  • Drive computational analysis in collaboration with wet-lab scientists to rapidly characterize and iterate on experimental methods by providing real-time assessments of assay performance, quality control, and clinical/diagnostic utility.
  • Build bioinformatics pipelines and bespoke computational tools to extract actionable information from high-throughput sequencing datasets for cancer classification.
  • Develop classification models using novel molecular/immunological signatures and evaluate feasibility translating these biomarkers to the product development phase.
  • Remain at the forefront of molecular techniques in onco immunology and develop scalable, automated laboratory and bioinformatic workflows to generate and analyze the data. 

Must haves:

  • PhD or equivalent experience in a relevant field such as molecular biology, computational biology, bioinformatics, or equivalent.
  • 3+ years post-PhD experience, industry experience in Immunological assays, protein (amino acid) sequence analysis, modeling, and embeddings. 
  • Strong computational and programming skills, including experience with Python statistical packages (Numpy, Matplotlib, Pandas).
  • Solid quantitative reasoning and statistical analysis skills, with a demonstrated ability to apply them effectively to relevant scientific problems.
  • Expertise in computational analysis of NGS datasets to derive technical and biological insights. Experience in protein display technologies or de novo antibody engineering/design is particularly relevant. 
  • Experience building and maintaining bioinformatics pipelines, with proficiency in common bioinformatics tools, such as FastQC, Bowtie, IGV.
  • Familiarity with software version control (Git) and common workflow languages (SnakeMake, Nextflow, Flyte). 
  • Experience implementing modern deep learning methods in biological sequence analysis for motif discovery and sample classification (convolutional and recurrent neural networks, HMMs, etc). 
  • Fundamental understanding of the central dogma, including background in molecular biology, cancer biology, and familiarity with regulation of molecular processes.
  • Familiarity working in a Linux server-based environment.
  • 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.

 

Benefits and additional information:

The US target range of our base salary/hourly rate for new hires is $182,750 - $230,000. You will also be eligible to receive pre-IPO equity, cash bonuses, and a full range of medical, financial, and other benefits dependent 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 @ https://careers.freenome.com/ 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.  

 

Tags: Bioinformatics Biology Classification Deep Learning Engineering Git Linux Machine Learning Matplotlib NumPy Pandas PhD Pipelines Python Research Statistics

Perks/benefits: Career development Equity Medical leave Startup environment

Region: North America
Country: United States
Job stats:  6  0  0

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