Matchpoint is a biotechnology company harnessing the power of covalency to discover precision covalent medicines to transform the treatment of immune diseases and other serious illnesses. The company's proprietary Advanced Covalent Exploration (ACE) platform integrates advanced chemoproteomics, machine learning and covalent chemistry library evolution. Matchpoint has an emerging pipeline of novel covalent medicines initially focused on immunology.
Founded in 2021 by Atlas Venture and Access Biotechnology together with leading scientists from the Dana Farber Cancer Institute and Stanford, we are rapidly growing our team and looking for passionate, driven, and creative thinkers to join us in ushering in a new era of covalent medicines.
Position Summary
Matchpoint Therapeutics is looking for an exceptional scientist who can develop and integrate cutting-edge data science solutions in collaboration with other scientists and engineers. The ideal candidate is an enthusiastic cross-functional collaborator. This person will work closely with colleagues from both the proteomics and the computational sciences teams to identify opportunities to apply data analytics and machine learning techniques to enhance Matchpoint’s technology platform by contributing to both our chemoproteomics platform and our drug discovery pipeline. This is a unique opportunity to help build a computational technology platform in a drug discovery setting.
Key Responsibilities / Essential Functions
- Develop statistical methods to analyze high-throughput chemoproteomics data and execute on such analyses
- Propose and develop statistical and machine-learning approaches to interrogate a vast library of chemoproteomics data to guide screening and pipeline efforts
- Cross-disciplinary work with the proteomics, biology, and chemistry teams to plan experimental design, lead methods development, and execute on pipeline-specific analyses
- Inter-disciplinary work with computational chemistry, cheminformatic, and bioinformatic teams to integrate various sources of data for key questions of interest
- Build interactive dashboards for data visualization and exploration
- Embed analyses and visualizations in automated reports
- Contribute to the further enhancement of the data science platform and infrastructure jointly with the data engineering team
- Stay current with the latest published developments in scientific data analytics, statistical methods, and machine learning approaches
Education Requirements/ Year(s) of Experience / Qualifications / Competencies
- Educational Requirements: Ph.D. in computational biology, bioinformatics, computational chemistry, or related computational sciences. Candidates without a Ph.D will not be considered.
- Year(s) of Experience: 2 - 8 years of relevant industry experience
- Qualifications: The following are required to have
- Knowledge of proteomics data processing pipeline such as Proteome Discoverer, Spectronaut, Skyline, Maxquant, Mass Pike, Frag pipe, MSstats.
- Knowledge of data generation, raw data analysis, as well as statistical and bioinformatic analysis of processed data to derive biologically meaningful insights
- A programming language, preferably in Python or R.
- Good communication skills and enjoys working in a highly integrated, cross-functional environment
- Direct experience analyzing chemoproteomics data highly preferred
- Competencies: Experience in two or more of the following:
- Analysis of -omics data and subsequent bioinformatic analyses like gene set testing
- Statistical modeling and design of experiments, for example sample size estimation, testing for group biases/enrichment, and meta-analyses.
- Building and validating machine learning models, preferably in an open-source machine learning platform such as TensorFlow, Pytorch, or DeepChem
- Data analysis across several data types and databases to produce clear and actionable results
- Supporting interdisciplinary drug discovery teams and high-throughput screening programs, especially covalent drug discovery programs
- Cheminformatics tools in analyzing high information content data sets
- Structural bioinformatics tools to analyze large data sets of proteins and protein-ligand complexes
- Building pipelines for quality control, processing and analysis of DIA and DDA data, including TMT and label-free quantification strategies
- Analyzing large scale protein interactions, protein turnover, and/or posttranslational modification studies.
Matchpoint is an equal opportunity employer, seeking to create a welcoming and diverse environment. All applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, marital or veteran status, disability, or any other legally protected status.
We offer a competitive compensation package, including equity-based compensation, annual bonus potential, healthcare benefits, 401(k), parking, and flexible paid time-off.
Matchpoint is an equal opportunity employer, seeking to create a welcoming and diverse environment. All applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, marital or veteran status, disability, or any other legally protected status.
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