Neomorph is a clinical-stage biotechnology company pioneering the discovery and development of molecular glue degraders to unlock new therapeutic possibilities across serious diseases. By engineering neomorphic protein surfaces, Neomorph enables precision targeted protein degradation of disease drivers that have historically been considered undruggable.

As a leader in molecular glue technology, Neomorph has created the world’s largest proprietary molecular glue target space, spanning multiple novel degrons across an expansive portfolio of E3 ubiquitin ligases. The company was founded in 2020 and is venture-backed by Deerfield Management Company.

Position Overview:

We are seeking an early to mid-career Data Analyst with strong quantitative training to support our scientific teams working in the molecular glue discovery space. Projects will focus on developing automated analysis methods that support large scale screening studies to identify new candidate compounds. This role is ideal for someone with an advanced degree in the mathematics sciences or related quantitative discipline who is excited to apply rigorous analytical methods to biological and experimental datasets. In this highly collaborative role, you will partner closely with biologists, and chemists, as well as receive mentorship from principal level data scientists.

Key Responsibilities:

Analyze high-dimensional biological datasets (e.g., screening data, assay readouts)

Develop reproducible workflows in Python, or similar tools to clean, transform, and analyze experimental data

Apply statistical modeling and hypothesis testing to evaluate experimental outcomes

Build dashboards and visualizations to communicate findings to cross-functional scientific teams

Partner with scientists to translate research questions into analytical frameworks

Contribute to building scalable data infrastructure and analytical standards across drug discovery programs

Required Qualifications:

Sr. Data Analyst:

Master’s or PhD degree in the mathematics sciences (e.g., mathematics, applied mathematics, operations research, statistics, data science)

At least 3 years of relevant professional or research experience
Data Analyst:

Master’s degree in the mathematics sciences (e.g., mathematics, applied mathematics, operations research, statistics, data science)

0 - 3 years of relevant professional or research experience (graduate level research, post-docs, industrial internships, or similar will be considered)

Both levels:

Strong programming skills in Python, R, or similar statistical languages

Solid understanding of statistical inference, regression modeling, and experimental design

Familiarity with SQL and working with structured datasets

Ability to communicate quantitative findings clearly to scientific collaborators

Passion for applying your quantitative skills set to scientific research, which has been demonstrated through course work, research, or prior work experience

Eagerness to work collaboratively in a team-oriented environment

Preferred Qualifications:

Exposure to biological data analysis (e.g., omics data, screening assays, bioinformatics)

Experience with visualization tools or interactive dashboards

Familiarity with machine learning approaches

Exposure to software development tools (e.g., version control, IDEs, containerization, cloud services, project management)