About Us
Valo Health is a technology company that is integrating human-centric data and AI-powered technology to accelerate the creation of life-changing drugs for more patients faster. Valo was created with the belief that the drug discovery and development process can and should be faster and less expensive, with a much higher probability of success. We are using models early to fail less often, executing clinical trials to add valuation to the company, and generating fit-for-purpose data to feed back into Valo’s Opal Computational Platform™ as we reinvent drug discovery and development from the ground up. Disease doesn’t wait, so neither can we.
We are a multi-disciplinary team of experts in science, technology, and pharmaceuticals united in our mission to achieve better drugs for patients faster. Valo is committed to hiring diverse talent, prioritizing growth and development, fostering an inclusive environment, and creating opportunities to bring together a group of different experiences, backgrounds, and voices to work together. We achieve the widest-ranging impact when we leverage our broad backgrounds and perspectives to accelerate a new frontier in health. Valo seeks to become the catalyst for the pharmaceutical industry and drive the digital transformation of the industry. Are you ready to join us?
About the Role
As aStaff Machine Learning Data Scientist in Epidemiology and Patient Data Products,you will be a core member on a team of data scientists building a powerful computational platform for advancing the discovery and development of new medicines. In this role, you will develop machine learning tools for patient data and expand their uptake across teams, under the guidance of epidemiology and biology program leads. Successful candidates will work with a diverse set of scientists and domain experts, in ways that cut across traditional industry boundaries in an innovative startup environment.
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More on Valo
Valo Health, Inc (“Valo”) is a technology company built to transform the drug discovery and development process using human-centric data and artificial intelligence-driven computation. As a digitally native company, Valo aims to fully integrate human-centric data across the entire drug development life cycle into a single unified architecture, thereby accelerating the discovery and development of life-changing drugs while simultaneously reducing costs, time, and failure rates. The company’s Opal Computational Platform™ is an integrated set of capabilities designed to transform data into valuable insights that may accelerate discoveries and enable Valo to advance a robust pipeline of programs across cardiovascular metabolic renal, oncology, and neurodegenerative disease. Founded by Flagship Pioneering and headquartered in Boston, MA, Valo also has offices in Lexington, MA, and New York. To learn more, visitwww.valohealth.com
- As a senior member of our team, you will lead knowledge translation machine learning models and predictions of patient data with diverse stakeholders. For example, integrate clinical insights into unsupervised cluster results and generate patient profiles.
- Perform hands-on analysis and modeling of highly dimensional longitudinal patient data, spanning electronic medical records, sequencing, and multi-omics, using R and Python in cloud environments.
- Contribute to the design, implementation, and evaluation of innovative machine learning approaches of patient data to provide novel clinical insights.
- Be comfortable with scientific uncertainty and embrace curiosity and creative solutions. Many of the challenges we’re trying to address don’t have known solutions or clear processes to arrive at answers.
- Use your technical knowledge and intuition to articulate and break down large problems into solvable pieces. There are a lot of problems to solve; you’ll need to prioritize which of these are critical path today from those that can wait.
- Be a dynamic and active team member, championing and adopting shared coding standards, participating in code review, and providing regular updates of your work and input into the work of your colleagues.
- MPH, MS with 5+ years or PhD in health sciences, biostatistics, or a quantitative field with 3+ years of work-related experienceapplying epidemiological, statistical, and/or machine learning methods to real-world datasets.
- Must have 3+ years of experiencedeveloping and implementing machine learning in health care databases including electronic health records, administrative claims databases, and/or patient registries. Familiarity with medical coding ontologies and data models in US and globally (ICD, ATC, LOINC, SNOMED, CPT, HCPCS, etc.).
- Extensive experience developing and maintaining machine learning pipelines and translating machine learning output into meaningful insights for diverse audiences.
- Confident in executing broad machine learning approaches, including random forest, logistic regression, dimensionality reduction, clustering, metrics, model selection, feature selection, and machine learning model explain ability.
- Proficient in Python (3+ years required) and experience with machine learning, deep learning, and data science packages (e.g., scikit-learn, pytorch, statsmodels, scipy, MLlib).
- Comfortable working in ambiguous problem spaces; experience working in a start-up or agile work environment as part of cross-functional project teams.
- Ability to lead and facilitate meetings and work collaboratively on multi-disciplinary project teams.
- Exceptional time management, ability to prioritize multiple tasks simultaneously, and deliver products on time every time.
- Enthusiastic about documentation–ensuring that all analyses are clear and reproducible with thorough documentation of key assumptions and decision points.
- Advanced knowledge of biostatistics approaches, including inferential and predictive modeling. Experience in causal approaches for observational studies, including propensity score methods, bias adjustment, and covariate selection and adjustment is a plus.
- Familiarity with or exposure to traditional drug discovery and development processes and approaches is a plus.
- Familiarity with integrated clinico–omics datasets (including sequencing, genomics, proteomics, etc.) is a plus.
Location: Remote
Salary range: None - None
