Top Entry Level Data Science Jobs in San Francisco Bay Area, CA

Apply statistical inference and experimentation to evaluate product changes, identify Growth opportunities, and guide strategy. Partner with product, engineering, and marketing leaders to define metrics, build datasets and dashboards, and communicate insights that drive business and product decisions. The role requires SQL, scripting, data transformation, and practical tradeoffs between speed and accuracy. Predictive modeling experience is preferred, as are startup and B2B SaaS backgrounds.

Embeds with strategic customers to design, build, and implement AI-driven solutions that scale support operations and drive measurable Fin adoption. Analyzes customer workflows and data, establishes measurement best practices, prototypes production-aware tools, and partners with Sales, Success, Product, and Engineering. The role also feeds deployment insights into product strategy and requires strong communication, adaptability, statistical analysis, SQL, Python, and experience with AI/ML evaluation or LLM applications.

Lead computational analysis for research on intrinsically disordered proteins and regions. Develop reproducible pipelines, integrate HDX-MS, interactomics, cryo-EM, and molecular-dynamics datasets, apply statistical and machine-learning methods, create visualization tools, support experimental design, and collaborate with structural biology, proteomics, and computational modeling teams. Contribute to publications, technical reports, and open computational resources.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Contribute to forecasting, matching, scoring, and machine learning systems powering a healthcare marketplace. Responsibilities include querying and wrangling Snowflake data with SQL, supporting A/B tests, applying statistical analysis, validating models, collaborating on MLOps and production deployment, and presenting findings to technical and non-technical audiences.

Develop and apply advanced AI and machine learning methods to aging biology research. Build computational approaches for large-scale biological and imaging data, including computer vision, segmentation, deep learning, generative AI, diffusion, multimodal, and foundation models. Analyze complex datasets, identify biologically meaningful patterns, collaborate with experimental and computational researchers, and help shape research at the intersection of AI, imaging, and aging.

Develop AI-enabled systems for harmonizing, modeling, and interpreting large biomedical and multi-omics datasets. Build LLM-powered tools, agentic workflows, RAG systems, data pipelines, and predictive models supporting computational biology and aging research. Collaborate with scientists and engineers on data curation, literature mining, annotation, reproducibility, documentation, manuscripts, and research software. Apply machine learning, network analysis, clustering, classification, and biological interpretation to transcriptomics, proteomics, metabolomics, clinical, imaging, and phenotypic data.

The job description does not include specific responsibilities or qualifications. The role is identified as Principal Data Scientist and may be performed remotely, in an office, or in a hybrid arrangement, depending on the employee’s location and Atlassian’s legal entity availability.

Own end-to-end data science for emerging content surfaces, including product experimentation, causal inference, exploratory analysis, metrics, dashboards, and strategic insights. Identify opportunities to grow content supply, creators, and engagement while improving discovery. Lead content data architecture and quality initiatives, develop source-of-truth assets, and apply advanced statistical methods such as quasi-experiments and network testing. Partner with Product, Engineering, Design, and Research to influence product direction and communicate findings to diverse audiences.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.

Build coupled fusion-device simulation models, radiation and surrogate models, diagnostic data pipelines, Bayesian inference systems, and uncertainty quantification tools. The role covers pulsed-power circuits, plasma dynamics, neutron and X-ray production, machine lifetime modeling, and HPC-enabled computation. The scientist will write production-grade numerical software, validate models against experimental diagnostics, document assumptions, and eventually grow a simulation team.