Top Entry Level Data Science Jobs

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.

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.

New-graduate Data Scientist role building and deploying ML and GenAI systems at scale for cybersecurity. Responsibilities include model development, fine-tuning LLMs, embedding-based search, production-ready code, automated tests, architecture reviews, and cross-functional collaboration to deliver AI-powered security capabilities.

Analyze and exploit GEOINT and supplementary data using advanced data science methods within GIS frameworks to support intelligence production. Develop approaches for intelligence questions, build dynamic geospatial web applications, and collaborate with scientists, engineers, and analysts. The role requires ArcGIS expertise, strong analytical and communication skills, and an active Top Secret clearance with SCI eligibility.

Develop user-facing financial AI and equity research algorithms across query understanding, document processing, entity recognition, event detection, retrieval, ranking, and quality evaluation. Build datasets, labeling systems, benchmarks, and production evaluation frameworks. Apply large language models, NLP, machine learning, reinforcement learning, knowledge graphs, and multimodal methods while collaborating with engineers to deploy and continuously optimize financial data and knowledge systems.

Develops statistical and data-mining analyses, analytical models, algorithms, and automated processes using large structured and unstructured datasets. Designs data acquisition systems, data structures, and databases; cleanses and integrates data with SQL, PHP, or Python; and supports reporting, product development, and manufacturing process insights. Collaborates with internal customers and project teams to gather requirements, consolidate data, and deliver analytics solutions.

Early-career data scientist role to analyze user and content data, build metrics, design recommendation strategies (recall, ranking, cold-start, diversity), run A/B tests, evaluate experiments, explore LLM/multimodal applications, and improve data pipelines and feature logging.

Develops statistical, machine learning, predictive, and advanced analytical solutions using structured and unstructured public health data. Integrates and analyzes data from surveillance, research, and administrative sources; evaluates data quality; creates visualizations and reports; monitors analytical tools; and translates findings into actionable public health insights. Collaborates with multidisciplinary CDC program teams and communicates technical results to technical and non-technical audiences.

Develop, deploy, and monitor predictive models and algorithmic decision systems for automated sports pricing and market making. Prepare data, build backtesting and simulation tools, analyze model and trading performance, and improve quoting, pricing, exposure management, and execution quality. Create dashboards and diagnostics while collaborating with traders, quantitative engineers, product, and engineering teams. The role includes occasional evenings, weekends, and holidays aligned with major sporting events.

Develop and deploy data-driven open banking solutions focusing on affordability/credit decisioning and identity/income verification. Apply end-to-end data science techniques to large datasets, build interactive dashboards with Plotly Dash, run customer trials, collaborate with engineers, and communicate results to stakeholders. Support reproducibility, automation, governance, and knowledge sharing within the Mastercard data science community.

Develop predictive models, quantitative analyses, algorithms, and visualizations using structured and unstructured data. Apply machine learning, artificial intelligence, natural language processing, and text-mining techniques to solve client challenges. Collaborate with clients and teammates to transform complex data into actionable insights, potentially supporting fraud detection, cancer research, and national intelligence missions.

Build AI, machine-learning, and statistical models to predict clinical-trial outcomes, regulatory results, and commercial potential. Analyze trial designs, endpoints, patient populations, treatment effects, safety, and subgroup data. Apply generative AI, LLMs, and NLP to scientific and regulatory information. Develop pipeline-monitoring tools, validate models, control bias and data leakage, and translate clinical and quantitative findings into investment conclusions supporting equity research, forecasts, valuations, and published analysis.

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.

Develop and implement AI and machine learning solutions for Corporate and Investment Banking Operations and Controls. Responsibilities include identifying and prioritizing use cases, building data products and models, collaborating with IT and business stakeholders, applying AI governance and risk controls, testing and monitoring solutions, creating documentation and training materials, and tracking KPIs, ROI, accuracy, and performance. The role also promotes AI best practices and adoption across the organization.

Leverage data science, machine learning, statistical analysis, and big-data techniques to solve business problems and support commercial strategy. Develop, deploy, and measure models, algorithms, automation, and analytical solutions. Collaborate with BI and enterprise data teams to improve reporting, visualizations, and data models. Present insights and recommendations to varied audiences while tracking business impact and evaluating emerging data engineering technologies.

Analyze and clean structured and unstructured data, assess data sources, build statistical and AI/ML models, and create Tableau visualizations and dashboards. The role develops actionable insights for national security decision-making, communicates findings through reports and briefings, researches analytic methods, responds to complex ad hoc analysis requests, and provides technical education to staff. The position is contingent upon contract award and requires an active Top Secret clearance.

Uses statistical modeling, machine learning, simulation, and visualization to solve business data problems. Responsibilities include identifying and ingesting data sources, evaluating data quality, building extraction pipelines, analyzing and visualizing data, developing predictive models, documenting reproducible analyses, gathering business requirements, managing priorities and dependencies, and presenting actionable insights to stakeholders. The role also supports developing data infrastructure that converts experiments into usable products.

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.

Develops machine learning, AI, statistical, and cheminformatics solutions for scientific R&D challenges. Builds predictive models, data pipelines, feature engineering workflows, generative AI capabilities, semantic search, and decision-support tools for chemical synthesis, process development, analytical sciences, and bioconjugation. Collaborates with multidisciplinary teams, independently drives projects from problem definition through validation, and communicates model results, uncertainty, assumptions, and limitations.

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.

Design and develop scalable data science algorithms using machine learning, operations research, optimization, simulation, and Generative and Agentic AI. Analyze massive datasets through exploratory analysis, feature engineering, and model building in cloud environments. Collaborate with data and software engineering teams to productionize solutions, quantify business impact, and mentor others as a technical coach.

Analyze defense and integrated combat system training, test, and evaluation data; develop MATLAB or Python scripts for data extraction and transformation; prepare technical reports and presentations; communicate findings to government stakeholders; document workflows; and collaborate with multidisciplinary teams supporting government-led weapons system assessments.

Develop predictive models and data science solutions for automated credit-risk and lending decisions. Responsibilities include collecting, cleaning, visualizing, and analyzing large datasets; identifying patterns; presenting insights to stakeholders; translating models into business strategies; partnering with IT on implementation; and monitoring model performance.

Develop and deploy machine learning models for fraud detection and risk assessment. Analyze large transactional datasets using exploratory analysis, Python, and data science libraries to uncover anomalies and patterns. Collaborate with engineering and business teams to integrate models into production, monitor performance, and improve algorithms. The role also involves researching emerging fraud detection and AI techniques.

Owns enterprise-level business and intelligence analysis by integrating structured and unstructured data, developing repeatable methodologies, creating Tableau visualizations, and translating complex findings into clear narratives. The role interprets data models, transforms data with Python or similar languages, applies quantitative and qualitative methods, documents workflows, coordinates customer requirements, and presents insights to technical and non-technical stakeholders. An active TS/SCI clearance with polygraph is required.