Top Entry Level Data Science Jobs

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.

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 predictive models and machine-learning solutions for cybersecurity, payment fraud detection, and vulnerability analytics. Analyze large datasets using distributed processing, identify patterns, create technical rules, integrate cybersecurity and transaction data, and design performance metrics. Collaborate with product, engineering, and operations teams, translate stakeholder needs into analytical solutions, and communicate insights to leadership. The role also involves graph databases and graph machine learning, including graph neural networks.

Supports data science teams by cleaning and processing structured and unstructured data, engineering features, executing and monitoring machine learning pipelines, generating insights, investigating trends and risks, and collaborating with senior data scientists and business analysts. The role requires a current MIT quantitative-field bachelor's student working 10–20 hours weekly in the continental United States.

Conducts quantitative research and experimentation for options market-making strategies. Responsibilities include building production testing pipelines, analyzing post-trade data, maintaining large tick-data datasets, presenting findings, and developing trading features and parameter configurations. The role requires learning options-market microstructure, exchange mechanics, networking, and FPGA-related concepts while collaborating with trading and engineering teams.

Develops and deploys predictive models and machine learning solutions for clients. Leads or mentors analysts, developers, and data consumers; designs cloud data environments, pipelines, statistical models, and data visualizations. Transforms structured and unstructured data into actionable insights, validates and engineers features, and advises clients on applying predictions to mission objectives. The role requires Secret clearance and a bachelor's degree, with DoD data platform and advanced clearance experience preferred.

Analyze SQL data using Python or R to identify trends, test statistical significance, and solve business problems. Present insights and recommendations to stakeholders, often collaborating with data engineers. Create visualizations in Tableau, adapt analytical approaches to business needs, and work within Agile teams and sprint-based delivery cycles.

Analyze federal workforce data and position descriptions using statistical modeling, NLP, machine learning, and text analytics. Develop dashboards, visualizations, and reports; map duties and skills to job series and grade levels; validate findings with HR professionals and leadership; and provide recommendations supporting accurate, compliant job classification and reclassification decisions.

Supports data science initiatives by developing algorithms and models against large datasets, analyzing business problems, and generating insights that improve profitability, efficiency, and customer experience. Communicates findings to technical and non-technical audiences, contributes to reports and presentations, and builds expertise in areas such as optimization, computer vision, recommendation systems, search, and NLP.

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.

Develop and maintain end-to-end computer vision systems, including model training, software serving, deployment, monitoring, scaling, and production iteration. Work across on-premise and cloud environments to support automated food distribution facilities. The role requires strong Python, computer vision, and machine learning skills, independent problem-solving, and collaboration with non-technical warehouse personnel.

Provides data science and systems engineering support for integrating COTS and AI/ML capabilities into customer environments. Supports cloud-based systems, multimedia forensics, deepfake creation and detection, publicly available information processing, and lifecycle activities. The role may also involve project management, AWS, Linux, and Microsoft Teams. An active TS/SCI clearance with polygraph and U.S. citizenship are required.

Analyzes structured and unstructured data to identify insights and solve client problems. Responsibilities include data exploration, cleaning, analysis, visualization, predictive modeling, quantitative analysis, algorithm development, and applying machine learning, artificial intelligence, or natural language processing. The role collaborates with clients and uses Python and other data technologies to produce objective, actionable answers for public- and private-sector challenges.

Develop predictive models and statistical frameworks for service cost forecasting, reliability risk, and long-term financial impact. Analyze service and installed-base data, quantify uncertainty, validate models, and improve forecasting accuracy. Partner with Service, Finance, Operations, and Engineering stakeholders to translate business needs into actionable insights. Build lightweight analytical prototypes and decision-support tools using AI-assisted development techniques, and communicate findings to technical and non-technical audiences.

Prototype and validate analytical methods and LLM applications for political, media, and advertising data. Build proof-of-concept tools, datasets, pipelines, and applications, then engineer validated methods toward production. Apply statistical and data science techniques, maintain rigorous software development practices, communicate findings clearly, and collaborate with technical and non-technical stakeholders.

Develop and support data pipelines using Python, SQL, PySpark, and cloud data platforms. Analyze data, prepare analytics-ready datasets, support reporting and machine learning initiatives, and contribute to AI-driven automation. Collaborate with engineers, data scientists, and business stakeholders while assisting with data quality, monitoring, troubleshooting, code reviews, and continuous improvement.

Analyze billions of user behavior, fund-flow, and market records using PySpark, Hive, Trino, and SQL. Build feature pipelines, investigate data anomalies, and develop and evaluate machine learning models for risk detection. Translate model performance into business metrics such as detection rate, false-positive rate, and latency while owning a project stream end to end.

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.

Supports equity research analysts by querying, transforming, analyzing, and combining large datasets; developing statistical models; building AI agents, MCP servers, and data workflows; and ensuring data quality, lineage, governance, and AI accessibility. The role translates complex technical methods into actionable insights, collaborates with research teams and vendors, and contributes to generative AI tooling and platform enhancements.

Supports equity research analysts by querying, transforming, analyzing, and combining large datasets; developing statistical models; and building AI tools, agents, MCP servers, and data processes. Ensures datasets meet accessibility, quality, lineage, and governance standards. Translates technical methods into actionable insights for nontechnical stakeholders while collaborating with research teams, vendors, management, and clients.

Analyze data to identify trends and anomalies, develop machine learning and statistical models, perform data parsing and ETL with Python and SQL, create dashboards, and collaborate with data engineers. The role emphasizes NLP, LLM, and Generative AI applications, quantitative analysis, model validation, and experimentation with emerging tools for federal clients.

Administers UMM data-management infrastructure, including access controls, configuration, dashboards, data flows, ingestion validation, monitoring, and security compliance. Provides advanced tier-two technical support, troubleshoots issues, supports testing and user acceptance, documents configurations, and coordinates enhancements with engineering and data stakeholders. The role supports distributed DoD and Intelligence Community users and requires on-site work in a DIA SCIF with an active TS/SCI CI Poly clearance.

Develops data pipelines, curates and analyzes large datasets, builds quantitative models, validates data and results, and deploys analytics solutions with product teams. Responsibilities include discovering useful datasets, ensuring data quality and reliability, supporting customer-driven requirements, and integrating solutions into interconnected systems. The role requires strong Python and data-handling skills, database expertise, large-scale batch processing experience, and U.S. citizenship.

Develops automated collection models, analytic models, workflow automations, and data-processing solutions supporting intelligence strategies. Evaluates and improves automated solutions using performance data and stakeholder feedback. Applies data science, visual programming, geospatial tools, statistical methods, machine learning, and visualization to address intelligence integration, automated collection, information sharing, and analytic workflow challenges.

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.