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Computational Scientist Rdkit Jobs (NOW HIRING)

Pfizer, Inc.

Senior Computational Chemist

La Jolla, CA · On-site

$106K - $176K/yr

Position Summary The Machine Learning & Computational Sciences (MLCS) organization within Medicine ... RDKit or equivalent). * Experience with standard data science packages (numpy, pandas, etc)

Pfizer

Senior Computational Chemist

La Jolla, CA · On-site +1

$106K - $176K/yr

Position Summary The Machine Learning & Computational Sciences (MLCS) organization within Medicine ... RDKit or equivalent). * Experience with standard data science packages (numpy, pandas, etc)

1910

Computational Theoretical Chemist I

Boston, MA

... science, and software engineering to develop drugs for previously undruggable targets. Role ... Familiarity with cheminformatics toolkits such as RDKit for molecular property prediction and data ...

Chemify Ltd

Staff CADD Scientist

San Francisco, CA · On-site +1

This gives our computational scientists something rare: a direct, high-throughput bridge from in ... RDKit, OpenEye); real experience inside the drug-discovery loop (SAR, MPO, DMTL cycles, lead ...

Schrodinger

Principal Scientist

New York, NY · Hybrid

$185K/yr

Directly supervise and provide instruction on computational skills to junior team members including ... RDKit, Plotly Dash, Argo Workflows, Jenkins, Django, Bayesian machine learning methods, Git and ...

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Computational Scientist Rdkit information

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$50.5K

$111.3K

$137.5K

How much do computational scientist rdkit jobs pay per year?

As of Jul 21, 2026, the average yearly pay for computational scientist rdkit in the United States is $111,343.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,500.00 and $137,000.00 per year, depending on experience, location, and employer.

What is a Computational Scientist specializing in RDKit?

A Computational Scientist specializing in RDKit is a professional who uses computational methods and the RDKit cheminformatics toolkit to analyze and model chemical structures and reactions. They often work in fields like drug discovery, materials science, or chemical engineering, leveraging RDKit for tasks such as molecular fingerprinting, property prediction, virtual screening, and data visualization. Their expertise combines advanced programming skills, a strong understanding of chemistry, and the ability to develop or optimize algorithms for chemical data analysis.

What are the key skills and qualifications needed to thrive as a Computational Scientist working with RDKit, and why are they important?

To thrive as a Computational Scientist using RDKit, you need a strong background in cheminformatics, computational chemistry, and programming, typically with an advanced degree in chemistry, bioinformatics, or a related field. Proficiency in Python, familiarity with RDKit libraries, and experience using molecular modeling and data analysis tools are essential. Critical thinking, problem-solving, and effective collaboration are important soft skills for translating scientific questions into computational solutions. These competencies enable accurate molecular data analysis, innovation in research, and successful teamwork in interdisciplinary environments.

What is the difference between Computational Scientist Rdkit vs Computational Chemist?

AspectComputational Scientist RdkitComputational Chemist
Required CredentialsDegree in Chemistry, Bioinformatics, or related; programming skills in Python; familiarity with RDKitDegree in Chemistry, Chemical Engineering, or related; strong background in molecular modeling and programming
Work EnvironmentResearch labs, biotech companies, pharmaceutical firms; focus on software development and data analysisAcademic or industrial labs; focus on experimental design, molecular simulations, and data interpretation
Industry UsageUsed in cheminformatics, drug discovery, and molecular data analysisApplied in pharmaceuticals, materials science, and chemical research

Computational Scientist Rdkit specializes in developing and applying cheminformatics tools using RDKit, often combining programming and data analysis. Computational Chemist focuses on molecular modeling, simulations, and chemical research. While both roles require chemistry knowledge and programming skills, the Computational Scientist Rdkit role emphasizes software development and data processing, whereas the Computational Chemist emphasizes experimental and theoretical chemistry applications.

How does a Computational Scientist working with RDKit typically collaborate with chemists and software engineers on research projects?

Computational Scientists specializing in RDKit often work closely with chemists to translate scientific questions into computational workflows, such as molecule property prediction or virtual screening. They also collaborate with software engineers to integrate RDKit functionalities into larger platforms or to optimize code for performance and scalability. Effective communication and project management are essential, as these interdisciplinary teams rely on regular meetings, shared documentation, and iterative feedback to ensure research goals are met efficiently. This collaborative environment not only fosters scientific innovation but also provides opportunities to learn from experts in related fields.
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Computational Scientist Rdkit jobs near you
Infographic showing various Computational Scientist Rdkit job openings in the United States as of July 2026, with employment types broken down into 3% Internship, 70% Full Time, 25% Part Time, 1% Temporary, and 1% Contract. Highlights an 70% Physical, 1% Hybrid, and 29% Remote job distribution, with an average salary of $111,343 per year, or $53.5 per hour.
Senior Scientist - Principal Scientist, Computational Chemistry

Senior Scientist - Principal Scientist, Computational Chemistry

Superluminal Medicines, Inc.

Boston, MA • On-site, Remote

Other

Re-posted yesterday

Job description

About the Role:

We are seeking a high-impact Computational Chemist to join our integrated discovery team. In this role, you will be the computational engine of our programs, combining physics-based modeling, machine learning and structural biology to generate the quantitative predictions and develop necessary workflows to drive small molecule drug discovery. You will serve as a core strategic partner to medicinal chemists and biologists, focusing on compound design and tool development to impact discovery pipeline and address unmet computational needs.

Key Responsibilities:

  • Integrate physics-based simulations with ML predictions to achieve the quantitative accuracy required to prioritize compounds for synthesis
  • Collaborate with a team of interdisciplinary scientists to develop actionable hypotheses and design computational experiments 
  • Design and prioritize chemical matter specifically aimed at hitting key program milestones, such as establishing in vivo POC, achieving selectivity windows, or optimizing ADMET profiles for candidate selection
  • Develop, validate and deploy computational workflows to optimize the  "Design-Make-Test-Analyze" cycles and address gaps

Required Qualifications:

  • Ph.D. in Computational Chemistry, Biophysics, or a related field
  • 1-3+ years of experience in a biotech or pharma setting performing computational support for small molecule drug discovery
  • Advanced knowledge of physics-based and ML computational chemistry packages including knowing when and how to deploy various tools for maximum project impact
  • Exceptional ability to communicate the "why" behind a design to a diverse scientific audience
  • Design experience working in concert with medicinal chemistry teams to design synthesizable compounds that efficiently work towards defined goals of activity, affinity, selectivity, properties, etc
  • A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones 

Preferred Qualifications:

  • Experience working with structural biology teams to extract the most information possible from cryo-EM and x-ray crystallography experiments and using this to accelerate programs using structure-based drug discovery techniques
  • Proven experience using ML to scale physics-based insights, specifically in the context of large-scale virtual screening or FEP-guided lead optimization
  • A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones   

Skills & Competencies:

  • Expert level use of structure-based small molecule drug discovery software tools including protein preparation, docking, FEP, QM, conformer selection. (Schrodinger suite, OpenEye, MOE, etc)    
  • Ability to work directly in a Linux-based environment
  • Familiarity with cloud computing infrastructure (AWS, GCS) is a plus
  • Python scripting and prototyping experience including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, etc)

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