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Computational Statistics Jobs (NOW HIRING)

Verge Genomics

Computational Biologist

San Francisco, CA · On-site +1

PhD in computational biology, AI/ML, applied statistics, biophysics, or , * MS and professional experience in relevant fields. * ≥5 years of experience working in applied computational biology and ...

Stanford University

Staff Scientist

Stanford, CA · On-site

Required : • Experience in functional or single cell genomics and/or statistical genetics • Strong experience in modern computational statistics and machine learning • Bachelor's degree and ...

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How much do computational statistics jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for computational statistics in the United States is $54.93, according to ZipRecruiter salary data. Most workers in this role earn between $46.88 and $73.56 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a computational statistician?

To thrive as a Computational Statistician, you need a solid background in statistics, mathematics, and computer science, usually supported by at least a master's degree in a related field. Expertise in programming languages such as R or Python, experience with statistical software, and familiarity with data management tools are typically required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you interpret data and present findings clearly. These skills are crucial for designing robust statistical models, extracting actionable insights from complex datasets, and supporting data-driven decision-making.

What are the common challenges faced by professionals in computational statistics, and how can they be addressed?

Professionals in computational statistics often encounter challenges such as managing large, complex datasets, ensuring computational efficiency, and translating statistical findings into actionable insights for non-technical stakeholders. Addressing these challenges typically involves staying updated with the latest software tools, collaborating closely with data engineers and domain experts, and continuously improving communication skills to explain technical results clearly. Proactively seeking opportunities for cross-functional teamwork and ongoing professional development can also help computational statisticians navigate these complexities and advance in their careers.

What is the difference between Computational Statistics vs Data Scientist?

AspectComputational StatisticsData Scientist
Required CredentialsDegree in Statistics, Mathematics, or related fieldDegree in Computer Science, Statistics, or related field
Work EnvironmentResearch labs, academia, data analysis teamsTech companies, consulting firms, diverse industries
Employer & Industry UsageAcademic institutions, research organizations, analytics teamsBusiness, technology, finance, healthcare
Common Search & Comparison IntentUnderstanding specialized statistical modeling and algorithmsBroader data analysis, machine learning, and business insights

Computational Statistics focuses on developing and applying statistical algorithms and models using computational methods, often emphasizing theoretical foundations. Data Scientists utilize these techniques along with programming and data manipulation skills to extract insights from large datasets across various industries. While there is overlap, Computational Statistics is more research-oriented, whereas Data Science covers a broader range of data analysis tasks.

What is computational statistics?

Computational statistics is a field within data analysis that uses algorithms, programming, and computer-based methods to process and interpret large datasets. It involves techniques such as simulation, optimization, and statistical modeling, often utilizing tools like R or Python. Professionals in this area develop and implement computational methods to solve complex statistical problems efficiently.
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Computational Statistics jobs near you
Infographic showing various Computational Statistics job openings in the United States as of August 2026, with employment types broken down into 79% Full Time, 19% Part Time, and 2% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution, with an average salary of $114,249 per year, or $54.9 per hour.

Computational Biologist

Verge Genomics

San Francisco, CA • On-site, Remote

Full-time

Re-posted 20 days ago

Job description

Who We Are
Verge is transforming drug discovery by using artificial intelligence and proprietary human data to solve the biggest driver of rising drug costs: high clinical failure rates. To achieve this, we have built one of the field's largest corpuses of multi-modal patient molecular and clinical data, sourced directly from human tissue. Our team of engineers, neuroscientists, and biologists have so far delivered two drugs to clinic, discovered 282 new targets, and signed commercial partnerships worth in excess of $1.6B with Eli Lily and AstraZeneca.
Your Mission
Reporting to the Head of Product & Engineering, and working alongside Verge's platform and computational biology teams, the Computational Biologist (AI/ML) will be responsible for defining and enabling new product offerings leveraging Verge's drug discovery engine for internal stakeholders, external partners (across both pharma and AI), and customers.
Your 12 Month Outcomes
  • Work with Verge's AI partners to deliver a best-in-class biology foundation model with Verge's proprietary datasets
  • Develop a novel approach that enables a powerful new product offering (patient stratification, biomarker discovery, etc.)
  • Deliver at least two CONVERGE-powered insights projects to pharma/biotech companies
  • Build an internal agentic AI workflow that supports multi-modal biomedical reasoning and orchestration

You Will
  • Develop and evaluate cutting-edge computational methodologies integrating multi-omic datasets to develop predictive models for translational biology,
  • Lead high-impact projects that apply and adapt AI models to translational challenges in disease biology, biomarker discovery, and target exploration,
  • Lead partnerships with AI companies to co-develop next-generation foundation models for drug discovery
  • Frame biological problems in computational terms and design solutions that are biologically meaningful, interpretable, and experimentally testable,
  • Design and implement evaluation methodologies for assessing AI model capabilities relevant to biological research and applications,
  • Translate between biological domain knowledge and machine learning objectives.

Requirements
Candidates must have:
  • Either:
    • PhD in computational biology, AI/ML, applied statistics, biophysics, or,
    • MS and professional experience in relevant fields.
  • ≥5 years of experience working in applied computational biology and integration of multi-omic datasets (RNA-seq, genotyping, clinical), with ≥2 years in a startup environment,
  • ≥2 years of experience in relevant areas of translational science, demonstrating a deep understanding of target identification, biomarker discovery, and/or patient stratification,
  • Proven ability to implement, evaluate, and/or create computational methodologies that leverage machine learning, statistics, and AI for biological research and discovery,
  • Fluency with state of the art in systems biology workflows, including off-the-shelf biological databases and computational biology tools,
  • Track record of bridging biological domain knowledge with computational approaches to solve real scientific problems
  • Track record of individual innovation, with published research or shipped work influencing pharma R&D decisions
  • Experience running a significant number of end-to-end RNA-Seq data analyses (from QC, read quantification, normalization through to interpretation),
  • Excellent coding skills in Python, with experience in relevant ML/AI libraries (e.g., PyTorch, HuggingFace, scikit-learn, pandas, numpy). A demonstrable portfolio (e.g., GitHub, research code, or shared notebooks) is highly preferred,
  • Experience in building and evaluating machine learning models on biological data, ideally with transformer-based models (e.g., scGPT, Geneformer, ESM, ProtBERT), with a deep understanding of feature selection, model interpretability,
  • Professional experience with AI workflows, including natural language processing (NLP), retrieval-augmented generation (RAG), embeddings, vectorization of diverse data types, and working with large language models (e.g., GPT),
  • Demonstrated experience with model evaluation and experimental design in a scientific context, including setting up appropriate benchmarks and controls.

Finally, we seek candidates who embrace our values and way of working:
  • Ability to thrive in uncertainty with frequently changing priorities
  • Deep alignment with our values
  • A passion for making an impact on patients

Apply