Staff Data Scientist, Computational Biology

Key Skills

Java Programming, Python Programming, Jupyter Notebook, MATLAB Programming, Kanban, R Programming

Job Description

Valo Health is a technology company applying human and machine intelligence to accelerate the creation of life-changing medical treatments. At the core of this vision is Valo’s computational platform: an end-to-end, integrated drug discovery and development engine that is being built from the ground up.

Valo hires the best and gives them first-class training and support. If you’re driven to perform, you’ll fit right in! We approach our work fearlessly, learn quickly, improve constantly, and celebrate our wins. A centerpiece of our culture is our commitment to inclusion across race, gender, age, religion, identity, and experience. Diversity fuels the Valo experience and drives us every day. We strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.

About the Role...

Valo is looking for a Staff Data Scientist to join the Computational Biology team to advance our preclinical programs and drive development of our drug-discovery platform. You will be on a team responsible for biostatistics, computational biology modeling, and machine learning that combines multi-omics data from diverse sources with Valo’s proprietary RWE (Real-World Evidence) and Computational Chemistry platforms in cardiovascular, metabolic, renal, and immune (CVMRI) domains. Successful candidates will work with a diverse set of scientists, entrepreneurs, and domain experts across traditional industry boundaries.

Valo’s Opal platform has state of the art computational chemistry, computational clinical, and biological platforms focused on CVMRI drug discovery, including data generation (laboratory & clinical trials), data platform (engineering), and software architecture support to enable novel compute- and human-first drug discovery and development. Opal is an AI-based platform that leverages human-centric data to enable researchers to discover and develop new drugs. Opal is a fully integrated, AI-powered, cloud-native platform that leverages human-centric data to create new approaches to drug discovery and development enabling researchers to minimize the cost and time associated with discovery, development, and delivery of novel therapeutics. The predictive insights produced by Opal rely on high-quality, high-density human-centric data that is sourced from multiple data sets, processed both remotely and on site through a highly complex process.

What You’ll Do…

• Advance existing preclinical drug discovery programs, working with data scientists, researchers, product teams, and other domain experts to build data-driven solutions to complex problems
• Provide Subject Matter Expertise (SME) in biological data processing to our Data Transformations and Platform team
• Contribute to the identification of novel targets and clinical biomarkers with Valo’s Reverse Translation Platform
• Model phenotypic responses to synthesized and simulated novel compounds
• Project phenotypic drug responses to RWE clinical impact

What You Bring…

• PhD in a computational science, data science, or related fields, with 3+ years of post-PhD experience (including post-doc) in collaborative settings to unravel complex biological problems and communicate domain knowledge to non-computational stakeholders & colleagues
• Experience in modeling multi-omic (2 or more: genomic, transcriptomic, proteomic, and/or metabolomic) data with machine learning methods & biological network analyses towards understanding biological functions and disease processes
• Machine learning (4 or more approaches which may include elastic nets, random forests, kernel SVMs, multi-level models, probabilistic graphical models, causal models, generative models) towards biologically-relevant predictive and causal modeling with high explainability
• An aptitude for learning about new domains and adapting machine learning techniques to those domains
• Strong analytical, problem-solving, and communication skills, including facility with Rmarkdown and/or Jupyter Notebooks for communicating reproducible results; and the ability to also condense, summarize, and synthesize those results into informative and actionable presentations to less technical audiences.
• Strong personal project management skills with significant practical experience managing your time split between multiple, parallel projects; experience with Agile processes and frameworks for team collaboration (e.g. Kanban, Atlassian tools)?
• Software engineering experience in R, Python, or Matlab, including familiarity with code, data, and model versioning