Data Scientist - Corradin Lab (Relocation Assistance Available)

MIT Whitehead Institute•Cambridge, MA

1d•$85,000 - $100,000

About The Position

The Data Scientist in the Corradin Lab at Whitehead Institute investigates human genetic and epigenetic variation linked to neurological disease. The role is suited to candidates with a strong interest in data science or computational biology, machine learning, and functional genomics. The Data Scientist designs and develops computational tools for biological data analysis, including single-cell RNA-seq, multiomics, and CRISPR perturbation experiments. The position analyzes experimental data, addresses methodological challenges in data integration, and uses discretion to prioritize and organize workflows and optimize methodologies and results. The role also develops and applies algorithms to large-scale biological data, performs hypothesis testing, optimizes next-generation sequencing data analysis, collaborates with internal and external partners, and leverages large language models and machine learning for genomics.

Requirements

PhD degree in biology or a data science-related field.
At least 2 years of experience in computer programming.
Proficiency in Python, Unix/Linux OS, and/or R.
Knowledge of statistical tests and machine learning algorithms.
Experience with data visualization in Python or R.

Nice To Haves

Experience processing NGS data.

Responsibilities

Design and develop computational tools for biological data analyses, including single-cell RNA-seq, multiomics, and CRISPR perturbation experiments.
Analyze experimental data and address methodological challenges of data integration.
Prioritize and organize workflow and optimize methodologies and results.
Develop and apply algorithms to analyze large-scale biological data across various projects.
Perform hypothesis testing using statistical concepts.
Use classification, regression, and clustering algorithms to address research questions.
Optimize the efficiency of NGS data analysis.
Work closely with internal and external collaborators.
Leverage LLMs and machine learning for genomics.
Perform other duties as assigned.