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Data Science Physics Jobs (NOW HIRING)

Roblox

Principal Data Scientist - Safety

San Mateo, CA · On-site

WHY DATA SCIENCE & ANALYTICS? The Data Science & Analytics organization's mission is to increase ... Advanced Degree and/or PhD in Statistics, Computer Science, Physics, Applied Math, Economics, or ...

GenLogs

Data Scientist

ABOUT THE DATA TEAM The Data Science team at GenLogs transforms raw observational data from the ... You bring a background in engineering, computer science, physics, applied math, or another hard ...

Equifax, Inc.

Data Scientist - Career

Atlanta, GA · On-site

... Data Science, Physics, Computer Science, Operations Research, Economics, Engineering or related quantitative field, PhD preferred - 5-7 years experience applying predictive analytics and modeling to ...

Databricks

Staff Data Scientist

San Francisco, CA

P-57 At Databricks, we are obsessed with enabling data teams to solve the world's toughest problems ... D. in quantitative fields (e.g., Statistics, Math, Computer Science, Physics, Economics ...

OpenX

Staff Data Scientist

D. in Data Science, Machine Learning, Computer Science, Physics, Mathematics, Operations Research, or related technical field with 6+ years of relevant industry experience; OR M.S./B.S. with 8+ years ...

OpenX

Staff Data Scientist

D. in Data Science, Machine Learning, Computer Science, Physics, Mathematics, Operations Research, or related technical field with 6+ years of relevant industry experience; OR M.S./B.S. with 8+ years ...

DEUNA

Data Science intern

San Francisco, CA · On-site

Data Science * Statistics * Mathematics * Engineering ... Economics * Physics * Or a related quantitative field. We may use artificial intelligence (AI ...

OpenX

Staff Data Scientist

D. in Data Science, Machine Learning, Computer Science, Physics, Mathematics, Operations Research, or related technical field with 6+ years of relevant industry experience; OR M.S./B.S. with 8+ years ...

OpenX

Staff Data Scientist

New York, NY · Remote

$195K - $218K/yr

Ph.D. in Data Science, Machine Learning, Computer Science, Physics, Mathematics, Operations Research, or related technical field with 6+ years of relevant industry experience; OR M.S./B.S. with 8+ ...

OpenX

Staff Data Scientist

New York, NY · On-site +1

Ph.D. in Data Science, Machine Learning, Computer Science, Physics, Mathematics, Operations Research, or related technical field with 6+ years of relevant industry experience; OR M.S./B.S. with 8+ ...

OpenX

Staff Data Scientist

New York, NY

Ph.D. in Data Science, Machine Learning, Computer Science, Physics, Mathematics, Operations Research, or related technical field with 6+ years of relevant industry experience; OR M.S./B.S. with 8+ ...

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All JobsData Science Physics Jobs

Data Science Physics information

What are the key skills and qualifications needed to thrive in the Data Science Physics position, and why are they important?

To thrive in Data Science Physics, you need strong analytical abilities in both physics and statistics, typically supported by an advanced degree in physics, data science, or a related field. Familiarity with tools such as Python, MATLAB, machine learning libraries (e.g., scikit-learn, TensorFlow), and experience using simulation or data visualization software are essential. Excellent problem-solving, collaboration, and communication skills help you work effectively with multidisciplinary teams and explain complex findings to non-experts. These competencies enable efficient analysis and interpretation of large scientific datasets, driving innovation and informed decision-making in research and industry settings.

What does a typical day look like for someone working in Data Science Physics?

A typical day in Data Science Physics often involves collecting, cleaning, and analyzing large datasets derived from experimental or simulated physics research. You may spend time developing and testing predictive models, interpreting results, and visualizing data to communicate findings to colleagues and stakeholders. Collaboration is common, with regular meetings alongside scientists, engineers, and data professionals to discuss project goals or troubleshoot challenges. Additionally, you may contribute to research publications or help develop new methodologies for data analysis, making each day varied and intellectually stimulating.

What is a Data Science Physics job?

A Data Science Physics job combines physics principles with data science techniques to analyze complex datasets, build predictive models, and extract insights. Professionals in this role apply statistical methods, machine learning, and computational algorithms to solve problems in areas such as material science, astrophysics, and engineering. They often work with big data, simulations, and experimental data to improve decision-making and research outcomes.

More about Data Science Physics jobs
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Data Science Physics jobs near you
Infographic showing various Data Science Physics job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.
Data Scientist, Finance Forecasting

Data Scientist, Finance Forecasting

ClickHouse

San Francisco, CA • On-site

Other

Re-posted 27 days ago

Job description

ClickHouse is the fastest open-source analytical database in the world, processing billions of rows per second for thousands of organizations. As we scale our cloud business, the decisions that shape pricing, capacity planning, and go-to-market strategy need to be grounded in rigorous quantitative modeling, and that capability is being built from the ground up.

We're hiring a founding Data Scientist to build ClickHouse's Finance forecasting and measurement capability from the ground up. You'll own and build the forecasting models, causal measurement programs, and analytical frameworks that directly shape how leadership plans the business. You'll define the approach, build the infrastructure, and set the standard for how data science operates here.

Hybrid: We intend to fill this role in the San Francisco Bay Area, and expect this position to go into one of our Bay Area offices, Menlo Park and San Francisco, 1-2x per week. 

What You'll Be Doing:
  • Own and build production revenue forecasting end-to-end: model development, backtesting, deployment, monitoring, and iteration
  • Build forecasting systems that account for the dynamics of usage-based pricing, consumption patterns, and customer lifecycle across our cloud platform
  • Design and implement causal measurement frameworks to quantify the revenue impact of product launches, pricing changes, and GTM motions
  • Establish backtesting discipline and accuracy tracking as standing Finance metrics, making forecast quality visible and continuously improving
  • Contribute to shared analytics infrastructure and internal tooling that accelerates data science workflows across the organization
  • Translate model outputs into clear, actionable recommendations for Finance, Sales, and executive leadership
  • Partner with Data Engineering, Revenue Operations, and Product to build the feature pipelines and data foundations your models depend on
What You Bring Along:
  • Has an advanced degree in a quantitative discipline (Statistics, Mathematics, Computer Science, Physics, Economics) or equivalent depth through production experience
  • Hands-on experience building and deploying ML and statistical systems, with meaningful time spent on forecasting or causal inference in production
  • Has deep applied statistics foundations, including comfort with time-series methods, state-space models, hierarchical approaches, or causal inference techniques
  • Is highly proficient in Python and SQL, with experience productionizing models in cloud-scale data environments
  • Has worked with modern analytical platforms such as ClickHouse, Snowflake, BigQuery, or Spark
  • Has experience forecasting consumption-based or usage-billed businesses (cloud, API, marketplace)
  • Has a bias toward action in ambiguous, early-stage environments and is comfortable defining the problem, not just solving it
  • Communicates clearly with executive stakeholders and can translate complex modeling work into actionable business recommendations
  • Is fluent with AI tools and workflows, including LLMs and AI coding assistants, and applies them effectively in analytical work
  • Is comfortable taking ownership of open-ended problems and building new functions from scratch

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