Top Data Science Jobs

Supports the design, development, deployment, testing, and monitoring of AI and machine learning solutions for business problems. Builds agentic AI prototypes, translates functional requirements into ML solutions, evaluates frameworks and algorithms, documents development standards, analyzes complex datasets, and communicates insights to technical and non-technical stakeholders. Projects may include demand forecasting, manufacturing optimization, ingredient formulation, yield management, and automated decision-making.

Build and maintain predictive models throughout their lifecycle, identify data patterns and business insights, develop analytic procedures and automation, perform complex ad hoc analyses, monitor model recalibration, and review model scoring and data transformation quality controls.

Build and maintain predictive models throughout their lifecycle, identify data patterns and trends, deliver decision-making insights, and develop analytic procedures and automation. The role also owns model recalibration, performs ad hoc analyses, addresses client strategy needs, and reviews model scoring and data transformation quality controls.

Own the optimization and decision science capabilities for Crucible, Firestorm’s manufacturing planning software. Develop production, sourcing, inventory, scheduling, allocation, and network optimization models; address uncertainty; validate and deploy production-ready solutions; and collaborate with software, manufacturing, supply chain, and product teams. Set technical direction, establish modeling standards, and build the foundation for future data science efforts.

Conducts healthcare data collection, validation, analysis, reporting, dashboard development, root-cause analysis, program evaluation, predictive modeling, and AI/ML work. Collaborates with stakeholders to define requirements, improve business outcomes, connect complex healthcare data sources, and communicate findings through reports, visualizations, and recommendations. Manages multiple projects while delivering accurate, timely results.

Develop predictive models, algorithms, quantitative analyses, and visualizations from structured and unstructured data. Collaborate with clients to understand business and mission needs, uncover insights, guide teammates, and translate complex datasets into actionable recommendations. The role uses programming, statistical analysis, machine learning, and distributed computing techniques across public and private-sector challenges.

Lead advanced analytics initiatives for operations, including lakehouse data integration, maintenance and work-order analytics, asset reliability modeling, inventory forecasting, anomaly detection, and NLP of technician notes. Build Databricks Apps and production ML workflows using Python, PySpark, SQL, MLflow, and Databricks technologies. Partner directly with operations stakeholders to translate business knowledge into data definitions and self-service analytical tools.

Design, build, evaluate, and productionize agentic AI, LLM, RAG, and predictive ML systems for scientific information products. Develop training and inference pipelines, fine-tune models, establish trustworthy-AI evaluation and safeguards, and operate AI capabilities at scale. Set engineering standards, lead technical initiatives, mentor scientists and engineers, influence product and executive decisions, and communicate results through presentations, publications, and client engagements.

Leads advanced data science initiatives by designing statistical and machine learning models, predictive analytics, forecasting, and probabilistic graph models. Develops and automates data pipelines using Python, PySpark, and R; deploys models with leading ML frameworks and MLOps tools; establishes evaluation and monitoring processes; collaborates with business teams; and mentors junior data scientists.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Supports Naval Surface Warfare Center mission engineering by applying data science, statistics, machine learning, data mining, graph algorithms, and operations research to electromagnetic spectrum data. Builds and maintains data pipelines and analytical systems, performs statistical investigations, supports software integration and testing, develops scripts and APIs, and converts large datasets into decision-quality insights.

Develop predictive models, algorithms, quantitative analyses, and visualizations from structured and unstructured data. Analyze complex datasets, apply statistical and machine learning techniques, and collaborate with clients to turn data into actionable insights for public- and private-sector challenges.

Develop and deploy predictive, statistical, and machine learning models for enterprise business decisions, including forecasting, segmentation, propensity modeling, optimization, and fraud detection. Partner with cross-functional stakeholders, improve cloud analytics infrastructure, evaluate data sources, communicate insights, and mentor junior data scientists and analysts.

Lead end-to-end dynamic pricing models using demand modeling, causal inference, market simulation, and optimization. Develop pricing approaches for elasticity, seasonality, pacing, and lead-time dynamics; design experiments measuring marketplace effects; and translate complex business problems into deployed solutions. Own production Python code, model deployment, monitoring, retraining, reproducibility, and ML operations while influencing product strategy and collaborating with technical and executive stakeholders.

Own mobile marketing analytics frameworks, dashboards, cohort and campaign analysis, profitability modeling, audience strategy, and incrementality experiments. Analyze attribution, retention, LTV, CAC, ROAS, payback, creative performance, and marketplace dynamics. Partner with marketing, finance, and leadership to guide spend allocation and growth decisions while navigating mobile privacy and attribution challenges.

Develops advanced data science solutions for Intelligence Community missions, including scalable data pipelines, automation, data modeling, statistical analysis, visualizations, dashboards, and production-quality Python applications. Transforms complex intelligence datasets into actionable insights, modernizes analytic workflows, supports operational decision-making, and collaborates with analysts, engineers, and mission stakeholders. The role also provides technical leadership, manages concurrent initiatives, and may apply geospatial analytics, machine learning, cloud platforms, and distributed computing.

Develops machine learning and deep learning models, analyzes large structured and unstructured datasets, identifies trends, and creates predictive solutions for business decisions. The role assesses AI/ML feasibility, builds data processes and prediction systems, provides analytical recommendations, collaborates with product teams and stakeholders, and supports innovative data strategies while following the bank’s risk management framework.

Develop and deploy advanced data science, machine learning, generative AI, and intelligent agent solutions across Salesforce and enterprise platforms. Build predictive models, conversational AI, data pipelines, and autonomous workflows for customer engagement, sales, and service operations. Partner with business and technical teams to define use cases, operationalize responsible AI, govern models, and deliver measurable outcomes. The role also involves evaluating emerging technologies, communicating insights to executives, mentoring developers, and establishing reusable technical standards.

Designs and deploys advanced machine learning, predictive analytics, generative AI, conversational AI, and intelligent agent solutions integrated with Salesforce. Builds data pipelines, features, models, and autonomous workflows using Agentforce, Einstein AI, and Data Cloud. Partners with business, product, engineering, and Salesforce teams to define use cases, govern models, monitor performance, and deliver measurable outcomes. The role also requires executive communication, technology evaluation, responsible AI practices, and mentoring developers.

Own end-to-end data science projects, including customer discovery, statistical analysis, predictive modeling, Python and SQL development, Databricks pipelines, AI-enabled analytics, quality checks, and customer presentations. Build reusable data assets at claims-data scale and contribute to Bonfire’s AI-powered products. The role requires rigorous analysis, strong communication, customer empathy, and comfort working with ambiguous problems.

Data Scientist Interns will automate processes, scrape and collect large datasets, and conduct advanced econometric analyses to investigate fraud across finance, healthcare, and cryptocurrency domains. The role involves applying data science and econometric techniques, contributing to individual and team projects, and translating academic expertise into practical industry applications.

Conduct fraud investigations across finance, healthcare, and cryptocurrency domains using data science and econometric techniques. Responsibilities include coding process automation software, scraping and collecting large datasets, performing advanced econometric analysis, and contributing to individual and team projects. The role offers exposure to industry applications of quantitative academic disciplines and collaboration with industry experts.

Provides data science consulting support to DHS by developing Power BI dashboards, cleaning and transforming data with Python and R, maintaining analytics solutions, and delivering predictive analytics and business intelligence. The role involves client communication, ad hoc analysis, requirements gathering, business development support, and translating technical topics for nontechnical audiences. Familiarity with cloud analytics platforms, PowerApps, ETL/ELT, financial data, and workload modeling is beneficial.

Uses advanced statistics, machine learning, experimentation, and causal inference to solve business problems involving platform engagement, product features, and games. Responsibilities include analyzing complex datasets, building predictive models, writing SQL queries, integrating data from multiple systems, designing experiments, and presenting actionable recommendations to stakeholders. The role also requires staying current with data science, generative AI, and machine learning developments.

Leads the design, development, deployment, and monitoring of machine learning and AI solutions. Frames complex business problems, engineers features, trains and validates models, conducts fairness and bias assessments, deploys production systems, and monitors drift and performance. Communicates results to leadership, mentors junior team members, and provides technical thought leadership on scalable ML solutions.