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

Develop personalization and machine learning solutions across product, growth, and monetization. Own projects from opportunity discovery and data preparation through modeling, experimentation, product integration, launch evaluation, and iteration. Build production scoring workflows, translate predictions into product actions, apply causal reasoning, and collaborate with engineering and product teams. Projects include recommendations, promotion targeting, retention, customer lifetime value, and lifecycle management.

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.

Create reproducible data science and machine learning tasks with reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify errors, data leakage, and flawed assumptions, then refine tasks accordingly. Document requirements, solution reasoning, and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical knowledge of machine learning and quantitative modeling.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Analyze product and operational data to guide roadmap decisions, define metrics, evaluate experiments, uncover user friction, and build reliable dbt models, dashboards, and analytical tools. The role partners closely with Product and Operations, improves data quality and instrumentation, and translates complex findings into actionable recommendations. It requires strong SQL, Python, experimentation, data modeling, visualization, and product judgment skills.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Develop and evaluate foundational deep learning methods, including transformers, embeddings, and neural networks, for credit modeling and loss prediction. Integrate models into ML pipelines, apply representation learning to financial data, and build specialized AI agents for data science and business workflows. Collaborate with technical and product teams, communicate findings, mentor colleagues, and measure model impact on customer and business outcomes.

Create reproducible data science and machine learning tasks with verified reference solutions, robust tests, and evaluation criteria. Investigate coding-agent failures, identify implementation errors and data leakage, refine task designs, and document requirements and verification steps. The role requires competitive data science experience, mathematical reasoning, programming ability, and practical expertise in implementing and validating solutions.

Build and validate forecasting, optimization, anomaly detection, and measurement models using complex customer data. Perform exploratory analysis, experiments, backtesting, and error analysis; partner with ML and data engineers to productionize models, features, evaluation pipelines, and monitoring. Translate business questions into analytical recommendations and communicate findings to technical and non-technical stakeholders. The role is hybrid in New York City or remote in the U.S. with monthly travel to New York.

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.

Supports the design, validation, and operationalization of predictive, machine learning, and AI models. Analyzes structured and unstructured data, develops algorithms, validates outcomes, creates visualizations, and communicates findings to stakeholders. Collaborates cross-functionally to integrate solutions and prepares model pipelines for business use. The role also involves statistical analysis, NLP, exploratory data analysis, feature engineering, and research into emerging technologies.

Designs challenging data science and machine learning tasks, builds reproducible environments and verified reference solutions, and develops automated tests and evaluation frameworks for AI-generated code. The role analyzes AI agent performance, identifies bugs, leakage, overfitting, and methodological weaknesses, documents technical requirements, and iteratively improves task reliability and complexity.

Build predictive and machine learning models for patient needs, operational forecasting, churn, advocate quality, and risk. Design A/B tests and causal inference studies, apply advanced statistical methods, productionize models with data engineers, and communicate insights to business stakeholders. The role requires Python, advanced SQL, forecasting expertise, statistical proficiency, data privacy awareness, and the ability to work independently in a fast-paced healthcare startup.

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.

Optimize dataset quality and value through marketplace incentives, task targeting, bonuses, payouts, eligibility, and experimentation. Design treatment assignments and holdouts, evaluate incremental collection against objective functions, identify Goodhart effects, and translate findings into operational actions. Partner with software teams to ship incentive and payout changes into the collection product. Experience with marketplace experimentation, pricing, growth, real-money payouts, or product implementation is preferred.

Partner with AI labs to improve frontier models by designing targeted data collection workflows, analyzing experimental results, and implementing quality controls. The role involves evaluating model behavior, reducing bias, supporting model safety and alignment, and translating technical findings into actionable insights for AI researchers and customers. Python is used for data analysis, pipeline development, and workflow prototyping.

Own Alpaca’s growth data strategy across acquisition, activation, retention, referral, and monetization. Build experimentation frameworks, causal and incrementality analyses, funnel and cohort models, LTV/CAC and churn forecasts, marketing measurement, governed growth metrics, and self-serve analytics. Partner with Product, Marketing, Sales, Engineering, and Finance to influence strategy and roadmaps, communicate insights to leadership, mentor data professionals, and establish standards for growth analytics.

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.

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.

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.

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.