Data Scientist I Roslindale 24 hours

Hebrew Rehabilitation Center•Roslindale, MA

15d•Hybrid

About The Position

The Data Scientist I is a biomedical researcher specializing in quantitative methods and research on the biology of aging. The role manages, cleans, harmonizes, and analyzes complex data, including DNA methylation and other multi-omic aging biomarker data from independently collected cohorts. It develops visualizations, conducts exploratory analyses, and prepares reports and summaries for colleagues and scientific audiences. Working with a biomedical research team, the Data Scientist supports study and experiment design, scientific publications, and dissemination of findings, with particular emphasis on developing, benchmarking, and validating epigenetic clocks and other aging biomarkers. The position contributes to project direction by formulating research questions, proposing study designs, and interpreting findings in biological and clinical contexts. It also develops algorithms and reproducible software and analysis pipelines for heterogeneous biomedical datasets, including exploring AI-agent-assisted harmonization and foundation-model approaches. The role supports observational and interventional aging studies, leads or participates in analyses of secondary datasets of varying sizes, contributes to funding applications, and provides data-analysis and interpretation support. The position is hybrid. Salary range: $67,133.00–$100,699.00.

Requirements

Doctoral degree in a quantitative biomedical science field such as bioinformatics, biostatistics, quantitative epidemiology, computational neuroscience, statistical genetics, econometrics, systems biology, or data science.
At least 2 years of experience as a professional quantitative scientist; postdoctoral research training in a related quantitative field satisfies this requirement. Relevant equivalent experience may substitute for academic training.
Familiarity with statistical analysis, software development, visualization, algorithm development, supervised and unsupervised learning, and predictive analytics.
Advanced data management, manipulation, and summarization skills, particularly for harmonizing DNA methylation and other omics data across cohorts into standardized, reproducible analytic platforms.
Knowledge of statistical and quantitative modeling, including methods such as regression, GLM/GAM, simulation, cluster analysis, neural networks, decision trees, bagging, boosting, and dynamical systems modeling.
Demonstrated proficiency in R and/or Python for large-scale DNA methylation and other omics data processing.
Specialized training and experience in more than one quantitative programming language, such as R, Python, or Matlab.
Existing or developable comfort with rigorous version control and reproducible research principles.
Ability to work in a team-based research environment and collaboratively across scientific, IT, and research operations teams.
Ability to manage complex multi-omic datasets; conduct statistical, machine-learning, and predictive analyses; and benchmark and validate aging biomarkers.
Ability to formulate research questions and testable hypotheses in aging biology and design analyses to address them.
Ability to write and publish peer-reviewed scientific manuscripts, organize analytic tasks and documentation reproducibly, and present results in spoken and written English.
Ability to work independently and across multiple projects with strict attention to detail, and to acquire new knowledge and skills in newly developed analytical methods and tools.
Sustained record of peer-reviewed publication, including first-author work, and strong oral and written English-language skills.

Nice To Haves

Skills in natural language processing and emerging AI-assisted data-harmonization tools.
Experience developing and maintaining harmonized, multi-dataset analytic platforms and benchmarking or validating aging biomarkers across cohorts.
Experience contributing to team leadership in quantitative analysis or machine learning.
Experience with database technologies such as SQL and REDCap.
Familiarity with AI-agent-based and foundation-model approaches to biomedical data harmonization.
Experience with web services and distributed-computing tools.
Experience using high-performance computing systems running Linux or UNIX and cloud platforms such as AWS or Google Cloud.
Passion for biomedical science and hands-on aging and geroscience research, including experience with epigenetic clocks or multi-omic aging biomarkers.

Responsibilities

Manage, clean, harmonize, and analyze complex multi-omic biomedical data, including DNA methylation and aging biomarker datasets from multiple cohorts.
Provide data-management and analytical support for aging research projects, including benchmarking and validating epigenetic clocks and other aging biomarkers across harmonized datasets.
Construct visualizations, conduct exploratory data analyses, and develop analytic reports and summaries.
Contribute to study design, including selection of cohorts, outcomes, and validation strategies.
Develop and apply algorithms for summary and analysis of complex, heterogeneous, multi-dataset data structures.
Participate in developing software tools and reproducible analysis pipelines, including exploring AI-agent and foundation-model methods for data harmonization.
Generate automated functions and pipelines for repetitive summaries and analyses.
Conduct statistical analyses to assess associations and support causal inference, as well as machine-learning and predictive analyses.
Lead or co-lead abstracts and manuscripts; contribute to funding applications; and present findings internally and at scientific meetings.
Maintain knowledge of geroscience and biomarker literature and propose analyses or research directions to Investigators.
Provide on-demand assistance with data analysis and interpretation.