Lab Manager and Bioinformatics​/Data Manager

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The Higgins-Chen lab at Yale University invites applications for a lab manager with excellent computer programming and data management skills. The lab uses AI and machine learning approaches to identify omics-based biomarkers to estimate the biological age of individuals, then adapt the biomarkers for use in clinical trials of geroscience-based interventions to prevent or treat age-related diseases. The lab also studies the interaction between aging and mental health.

Lab members primarily perform computational analysis, collaborating with clinicians, experimental biologists, epidemiologists, social scientists, and industry to train and validate biomarkers.

The lab manager’s top priority is to further develop and manage the lab’s code and multi-omic data repositories for lab research projects. The lab manager must have full‑stack development and SQL database management skills. Code, data, and associated documents must be well‑organized and documented, to ensure high quality and reproducibility of bioinformatic studies, provide easily accessible information to lab members writing papers and grants, and ensure the lab complies with regulatory, data privacy, and data sharing requirements.

We aim to scale the code and data to new contexts (e.g. multimodal data, multispecies data), and make it easily interoperable with LLMs and AI agents. The lab manager will also assist Dr. Higgins‑Chen in identifying new datasets for biomarker studies, applying for data, obtaining data, managing data use agreements, and ensuring lab compliance with data use agreements.

Depending on interest and time, the lab manager can focus additional efforts on their own specific research project and/or collaborate with other lab members. The lab manager is highly encouraged to write research papers on software packages and databases they develop. The lab manager also receives authorship credit on publications using code or data that play a substantial role in developing or managing.

• Proficiency in at least three of the following: full‑stack development, SQL database management, Git Hub repository management, and developing R and Python.
• Demonstrated experience implementing new systems for data and code management to ensure reproducible research in a laboratory setting.

• Prior experience working in a research lab, preferably with a significant computational component.
• Experience with LLMs and AI agents and a variety of supervised and unsupervised machine learning techniques.
• Experience with genomic analysis pipelines (e.g. for genome sequencing, methylation, proteomics, metabolomics assays). Experience with analyzing wearable device data.
• Experience with High Performance Computing (HPC) clusters.
• Skills helpful to other lab members in analyzing data or publishing papers (writing, editing, graphic design, etc.).

• Evaluates and analyzes data using accepted statistical and biostatistical techniques.
  2. Investigates, analyzes, and evaluates complex statistical and programming problems. Determines proper methodology, testing standards, and evaluation processes for research projects. Recommends and develops statistical approaches for use in analyses.
• Prepares analysis plans and writes detailed specifications for analysis files, consistency checks, tables, and figures; communicates with clients regarding statistical analysis issues.
• In collaboration with research investigators, contributes to the design of research studies, develops analytical plans, conducts statistical analysis and interpret the results.
• Ensures the integrity of databases used in analyses through development of essential data cleaning and checks, and data back‑ups.
• Plans statistical programming activities and schedules to provide investigators with…