Top Data Science Jobs

Build and productionize end-to-end fraud and financial risk models: data acquisition, feature engineering, labeling, training, experimentation, deployment, and monitoring. Research new fraud types, drive product development, support data quality and access, and present analyses to inform product, operations, sales, and marketing.

Early-career data scientist supporting sector investment teams: deliver data pulls, analyses, trackers, dashboards, anomaly investigations, and reusable Python/SQL tooling. Use time-series methods, alternative data, and LLMs to accelerate workflows and build commercial judgment with investors.

Lead product analytics efforts by designing and evaluating experiments, building dashboards, and producing statistical insights. Partner with product, design, and engineering to measure product success, identify optimization opportunities, and embed ML-driven personalization into the product.

Develop scalable statistical and data science solutions for monitoring emerging vehicle safety issues. Analyze large, complex datasets using time series, categorical, multivariate, and sampling methods; support anomaly detection, diagnostics, prognostics, and root cause analysis. Automate analytical workflows, improve safety investigations, communicate findings to stakeholders and senior leadership, and mentor less experienced team members.

Build and productionize traditional machine learning models, owning feature engineering, validation, deployment, monitoring, drift detection, and retraining. Develop LLM-powered agentic tools, dashboards, self-serve analytics products, and automated insight pipelines. Partner with engineering and business stakeholders, write production-grade Python and SQL, drive adoption, improve data quality, and establish responsible AI workflows for a regulated industry.

Perform data cleaning, preprocessing, integration, and quality assurance; create complex visualizations and dashboards (Tableau/Power BI); run routine data quality checks; and apply AI/ML models to analyze unstructured data to support mission-critical decision making.

This role involves leading data science projects, developing machine learning models, collaborating with stakeholders, and mentoring other data scientists.

As a Data Scientist, you'll leverage data to drive corporate strategy by building machine learning models and collaborating with cross-functional teams.

Own Fora’s growth operating model for forecasting, scenario planning, goal-setting, and strategic investment decisions. Partner with Strategy & Operations, Finance, founders, and executives to analyze business trends, size opportunities, model pricing and initiative impacts, and guide resource allocation. Mentor data team peers while establishing best practices, tools, processes, and standards.

Lead product analytics and experimentation for Fora’s travel marketplace. Analyze product usage and advisor behavior, define data instrumentation, guide roadmap decisions, build predictive and personalized models, and establish experimentation best practices. Partner closely with Product, Engineering, and Design while mentoring data team peers and improving processes, tools, and standards.

Lead marketing measurement and growth analytics through incrementality experiments, attribution, media mix modeling, customer value and retention modeling, and production machine learning. Partner with acquisition, lifecycle, membership, and growth leaders to guide investment and strategy. Mentor data teammates and establish best practices, processes, tools, and standards.

Summer 2027 internship supporting Sector Data Scientist and investment teams by acquiring, cleaning, and analyzing multiple data sources, testing investment hypotheses, identifying key data drivers and inflection points, and presenting research and decision-support tools to inform investment decisions.

Lead data science efforts for a defense program onsite in Hazelwood, MO. Define data requirements, build analysis systems, design/deploy ML models, collaborate with engineers and SMEs, mentor junior scientists, ensure security/compliance, and communicate results to stakeholders while improving models and establishing best practices.

Lead development and independent validation of advanced credit risk models used for lending criteria and verification. Prepare validation reports, monitor model performance, support production releases, and improve validation methodologies through automation, data manipulation, machine learning, and programming. Provide technical guidance and mentorship while communicating quantitative findings to technical and business stakeholders.

Lead product-focused quantitative analyses and machine learning efforts to generate actionable insights, drive product decisions, and optimize features. Collaborate with cross-functional teams to design metrics, build scalable statistical solutions, create dashboards, and communicate findings while using AI tools responsibly.

Lead payments-focused data science work: build foundational datasets, metrics, and scalable analytics; analyze customer and transaction behavior (including onchain data); conduct strategic analyses to identify product and revenue opportunities; build reporting, dashboards, and automation; and influence product and business strategy through data storytelling to senior stakeholders.

Turn data into product and business decisions through metric design, experimentation, causal analysis, data modeling, deep-dive analysis, dashboards, and data storytelling. Partner with Product, Finance, GTM, Growth, and Engineering leaders to measure impact, influence priorities, and improve company-wide data practices. Model analytical data in dbt, collaborate with data engineering, and help shape an AI-first data team.

Technical lead for Growth Analytics: partner with Growth/Marketing/Sales to identify opportunities, design experiments and statistical models, build scalable measurement frameworks, mentor junior data scientists, translate business questions into analytic solutions, and communicate insights to drive acquisition, activation, retention, and reactivation.

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 and maintain data-driven solutions using statistical, machine learning, and AI techniques. Responsibilities include data preparation, exploratory analysis, predictive modeling, deployment support, monitoring, documentation, and communicating results. The role collaborates with technical and business stakeholders, participates in code reviews, researches emerging technologies, and applies modern development tools. Candidates should demonstrate growing independence, strong Python skills, and foundational data science expertise, with desirable exposure to cloud platforms, MLOps, generative AI, CI/CD, and containerization.

Lead advanced analytics and modeling efforts: develop queries, prepare data, design and validate predictive and prescriptive models, mentor junior staff, engage stakeholders, deliver insights and recommendations, and apply modern data science and big-data techniques across business functions.

Develop and maintain data infrastructure, metrics, software tools, and processes for evaluating Starlink’s network growth and performance. Lead investigations into network issues, automate metric aggregation and application issue detection, build data ingestion and ETL systems, apply analytics and machine learning, and fuse multiple data sources. The role collaborates closely with engineering teams and external partners in a dynamic environment.

Support data-driven decision-making by collecting, cleaning, and analyzing structured and unstructured data, performing EDA, assisting in building and validating ML and statistical models, creating visualizations and dashboards, documenting findings, and collaborating with cross-functional teams.

The Data Scientist will support Army network and enterprise information environment missions by analyzing complex datasets, developing statistical and machine learning models, integrating APIs and automated data pipelines, and building Palantir Foundry and Army Vantage applications. The role includes creating executive visualizations, applying AIP tools, supporting secure Army and DoD data environments, and translating technical findings into actionable recommendations for senior leadership.

Conduct statistical analysis, design experiments, clean and harmonize data, and develop predictive and machine-learning models. Analyze customer, transaction, campaign, clinical, and non-clinical data to identify trends, segmentation opportunities, and actionable insights. Interpret and visualize model results for technical and healthcare stakeholders, collaborate cross-functionally on data-driven solutions, and support ETL processes involving Salesforce CRM platforms.