Valo Health
Staff Data Scientist in Epidemiology and Patient Data Products
Valo Health, Lexington, Massachusetts, United States, 02173
Staff Data Scientist in Epidemiology and Patient Data Products
About Us
Valo Health is a human‑centric, AI‑enabled biotechnology company working to make new drugs for patients faster. The company's Opal Computational Platform transforms drug discovery and development through a unique combination of real‑world data, AI, human translational models and predictive chemistry.
Our talented team of biologists, chemists and engineers, armed with advanced AI/ML tools, work together to break down traditional R&D silos and accelerate the speed and scale of drug discovery and development.
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 embrace new ways of learning, solve complex problems and welcome diverse perspectives that can help us advance patient‑centric innovation.
Valo is headquartered in Lexington, MA, with additional offices in New York, NY and Tel Aviv, Israel. To learn more, visit www.valohealth.com.
About the Role As a
Staff Data Scientist in Epidemiology and Patient Data Products , you will be a core member of a team of data scientists advancing the discovery and development of new medicines. In this role, you will answer research questions using large real‑world healthcare databases to inform identification of biological molecules for effective drug development under the guidance of epidemiology program leads. To do so, you will work in partnership with colleagues in machine learning, statistical genetics, and computational biology to develop solutions to challenging computational problems. Successful candidates will work with a diverse set of scientists and domain experts and engage with external partners, in ways that cut across traditional industry boundaries in an innovative startup environment.
What You'll Do
Lead real‑world data studies (e.g., electronic medical records) from end‑to‑end to generate causal evidence for projects in drug discovery and development.
Translate research questions into observational study designs to generate patient‑centric insights from statistical models.
Curate clinical and non‑clinical variables for machine learning models.
Execute trajectory modeling techniques using real‑world data.
Interpret machine learning results into patient profiles.
Execute post‑hoc longitudinal analyses among patient profiles of interest.
Be comfortable with scientific uncertainty and embrace curiosity and creative solutions.
Work with diverse data spanning electronic medical records, sequencing, multi‑omics data, and other modalities using R and Python in cloud environments.
Use technical knowledge to articulate and break down large problems into solvable pieces, prioritize critical‑path work.
Collaborate with drug discovery and clinical development teams to ensure relevance and impact of insights.
Champion shared coding standards, participate in code review, and provide regular updates of work and input into colleagues' work.
What You Bring
MPH, MS with 5+ years or PhD in epidemiology or biostatistics with 3+ years of work‑related experience
applying epidemiological, statistical, and/or machine learning methods to real‑world datasets.
Must have 3+ years of experience
developing and executing robust analytical strategies, including cohort and case‑control study design, using health care databases such as electronic health records, administrative claims, and/or patient registries.
Experience leading epidemiologic projects from end‑to‑end : translating research questions into observational study designs, contrasting strengths and weaknesses of different study designs and statistical approaches, and generating patient‑centric insights from statistical models.
Extensive experience with causal approaches applied to observational studies, including propensity score methods, bias adjustment, and covariate selection and adjustment.
Advanced knowledge in biostatistics approaches, including inferential and predictive modeling, and comfortable implementing unsupervised machine learning algorithms in real‑world health care databases.
Must have experience conducting data manipulation and statistical analysis in Python and/or R programming languages.
Comfortable working in ambiguous problem spaces; experience working in a startup or agile 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.
Enthusiastic about documentation—ensuring that all analyses are clear and reproducible with thorough documentation of key assumptions and decision points.
You May Also Bring
Research experience in obesity, cardiometabolic, and/or neurodegenerative therapeutic areas.
Experience developing and maintaining machine learning pipelines, and translating machine learning output into meaningful insights for diverse audiences is a plus.
Familiarity with or exposure to traditional drug discovery and development processes and approaches is a plus.
Hands‑on experience curating structured health data and working with health data from outside the U.S.
Compensation
Remote Salary Range: $153,000—$200,000 USD
CA Salary Range: $180,540—$236,000 USD
Compensation for the role will depend on a number of factors, including a candidate's qualifications, skills, competencies, and experience. Valo Health currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Valo Health's good faith estimate as of the date of publication and may be modified in the future.
Please note: At this time, we are only able to consider candidates who currently have permanent US work authorization without the need for immediate or future sponsorship.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Engineering and Information Technology
#J-18808-Ljbffr
Valo Health is a human‑centric, AI‑enabled biotechnology company working to make new drugs for patients faster. The company's Opal Computational Platform transforms drug discovery and development through a unique combination of real‑world data, AI, human translational models and predictive chemistry.
Our talented team of biologists, chemists and engineers, armed with advanced AI/ML tools, work together to break down traditional R&D silos and accelerate the speed and scale of drug discovery and development.
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 embrace new ways of learning, solve complex problems and welcome diverse perspectives that can help us advance patient‑centric innovation.
Valo is headquartered in Lexington, MA, with additional offices in New York, NY and Tel Aviv, Israel. To learn more, visit www.valohealth.com.
About the Role As a
Staff Data Scientist in Epidemiology and Patient Data Products , you will be a core member of a team of data scientists advancing the discovery and development of new medicines. In this role, you will answer research questions using large real‑world healthcare databases to inform identification of biological molecules for effective drug development under the guidance of epidemiology program leads. To do so, you will work in partnership with colleagues in machine learning, statistical genetics, and computational biology to develop solutions to challenging computational problems. Successful candidates will work with a diverse set of scientists and domain experts and engage with external partners, in ways that cut across traditional industry boundaries in an innovative startup environment.
What You'll Do
Lead real‑world data studies (e.g., electronic medical records) from end‑to‑end to generate causal evidence for projects in drug discovery and development.
Translate research questions into observational study designs to generate patient‑centric insights from statistical models.
Curate clinical and non‑clinical variables for machine learning models.
Execute trajectory modeling techniques using real‑world data.
Interpret machine learning results into patient profiles.
Execute post‑hoc longitudinal analyses among patient profiles of interest.
Be comfortable with scientific uncertainty and embrace curiosity and creative solutions.
Work with diverse data spanning electronic medical records, sequencing, multi‑omics data, and other modalities using R and Python in cloud environments.
Use technical knowledge to articulate and break down large problems into solvable pieces, prioritize critical‑path work.
Collaborate with drug discovery and clinical development teams to ensure relevance and impact of insights.
Champion shared coding standards, participate in code review, and provide regular updates of work and input into colleagues' work.
What You Bring
MPH, MS with 5+ years or PhD in epidemiology or biostatistics with 3+ years of work‑related experience
applying epidemiological, statistical, and/or machine learning methods to real‑world datasets.
Must have 3+ years of experience
developing and executing robust analytical strategies, including cohort and case‑control study design, using health care databases such as electronic health records, administrative claims, and/or patient registries.
Experience leading epidemiologic projects from end‑to‑end : translating research questions into observational study designs, contrasting strengths and weaknesses of different study designs and statistical approaches, and generating patient‑centric insights from statistical models.
Extensive experience with causal approaches applied to observational studies, including propensity score methods, bias adjustment, and covariate selection and adjustment.
Advanced knowledge in biostatistics approaches, including inferential and predictive modeling, and comfortable implementing unsupervised machine learning algorithms in real‑world health care databases.
Must have experience conducting data manipulation and statistical analysis in Python and/or R programming languages.
Comfortable working in ambiguous problem spaces; experience working in a startup or agile 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.
Enthusiastic about documentation—ensuring that all analyses are clear and reproducible with thorough documentation of key assumptions and decision points.
You May Also Bring
Research experience in obesity, cardiometabolic, and/or neurodegenerative therapeutic areas.
Experience developing and maintaining machine learning pipelines, and translating machine learning output into meaningful insights for diverse audiences is a plus.
Familiarity with or exposure to traditional drug discovery and development processes and approaches is a plus.
Hands‑on experience curating structured health data and working with health data from outside the U.S.
Compensation
Remote Salary Range: $153,000—$200,000 USD
CA Salary Range: $180,540—$236,000 USD
Compensation for the role will depend on a number of factors, including a candidate's qualifications, skills, competencies, and experience. Valo Health currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Valo Health's good faith estimate as of the date of publication and may be modified in the future.
Please note: At this time, we are only able to consider candidates who currently have permanent US work authorization without the need for immediate or future sponsorship.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Engineering and Information Technology
#J-18808-Ljbffr