Top Data Science Jobs

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.

Develop and manage production-grade machine learning models for wholesale power and gas markets, including price forecasting, congestion analysis, load forecasting, and trading algorithms. Own the ML lifecycle from feature engineering and model selection through deployment, monitoring, and refinement. Analyze ERCOT and PJM market drivers, build data visualizations, apply LLMs, collaborate with technical and business stakeholders, and communicate insights that support trading, risk mitigation, and operational decisions.

Leads end-to-end applied data science and AI workstreams for predictive underwriting and pricing, including problem framing, feature engineering, model development, validation, deployment, monitoring, and business-impact measurement. Builds explainable, governed, and reusable production models using healthcare and insurance data. Partners with underwriting, business, product, engineering, and legal teams; mentors others; and advances responsible AI, MLOps, model governance, and shared data science practices.

Build and deploy machine learning models for supply chain, inventory, demand forecasting, pricing, promotion, clearance, and network optimization. Develop time series, predictive, simulation, and discrete optimization models; create scalable pipelines using Python, Spark, and cloud infrastructure; and own the full model lifecycle. Partner with engineering, product, and operations teams, communicate insights, develop decision-support tools, mentor junior data scientists, and measure business impact.

Own end-to-end data science initiatives across growth, personalization, pricing, market execution, risk, financial integrity, and financial crime. Build causal inference, predictive, ranking, and fraud detection models; deploy and monitor them in production; communicate uncertainty and insights to senior stakeholders; and mentor teammates on statistical and modeling best practices.

Develop model-ready datasets and applied machine learning models using biologics assay, biophysical, antibody, protein sequence, and construct data. Curate and version datasets, create featurization and evaluation frameworks, and collaborate with data engineers, discovery scientists, MLOps teams, and molecular modelers to support trustworthy, reproducible biologics discovery and prioritization.

As a Data Scientist, you'll develop and validate machine learning models, implement AI solutions, and conduct fieldwork in support of customers.

Own applied statistical analysis for mineral processing operations, including experimental design, metric definitions, uncertainty quantification, process control, forecasting, reporting, and dashboards. Analyze messy sensor, assay, laboratory, and plant data to support production, cost, supplier, and capacity decisions. Partner with data platform teams, improve analytical rigor across the company, and communicate findings clearly to operators, engineers, and executives.

Develop analytical and AI-enabled capabilities for a federal data modernization effort. Translate stakeholder questions into statistical analyses, predictive models, forecasts, and scenario analyses; build governed datasets using Databricks, BigQuery, Python, SQL, and PySpark; validate and document models; maintain analytical products; and communicate visualizations, dashboards, findings, assumptions, and limitations to technical and non-technical audiences.

Lead data science research and production development for attribution, identity resolution, tracking quality, and incrementality measurement. Design experiments, build and monitor production pipelines, evaluate third-party identity vendors, and develop ML and graph-based solutions. Translate technical findings into recommendations for Product, Engineering, and leadership while shaping measurement strategy and build-versus-buy decisions.

Analyze complex structured and unstructured data, build and maintain data pipelines, develop predictive and prescriptive models, and manage machine learning projects. The role uses statistical methods, NLP, RPA, text mining, forecasting, and cloud-based data platforms to support Special Operations Command North. Responsibilities include data engineering, algorithm development, visualization, experimentation, digital transformation, and communicating analytical findings to nontechnical stakeholders.

Own production NLP enrichment systems for scientific literature at scale. Build pipelines extracting entities, classifications, claims, and summaries; compare classical NLP, embeddings, and LLM approaches; develop evaluations and golden datasets; and ship reliable, cost-efficient Python systems. Collaborate with data engineers using Airflow and Dagster, contribute to agentic AI applications, and partner with editors, product managers, and engineers to translate modeling needs into product outcomes.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Lead Data Scientist partners with business leaders to identify opportunities, design, develop, and deploy advanced analytics and ML solutions. Responsibilities include data preparation, modeling, feature engineering, delivering insights and visualizations, establishing standards, and directing associate data scientists, contractors, and consultants to produce measurable business value.

Develops and maintains data-driven tools for Diplomatic Security risk and threat assessments. Collects, integrates, analyzes, and securely manages data throughout its lifecycle; builds hardware and software solutions; analyzes alarm and operational data; improves workflows and procedures; manages system configuration and releases; delivers executive briefings; and supports interagency coordination involving classified national security information.

Lead data science and analytics initiatives supporting product, engineering, leadership, and insurance clients. Analyze claims and product data, design experiments, generate hypotheses, measure ROI, build dashboards and reproducible analytics, and communicate actionable recommendations. Mentor junior analysts, conduct client-facing analyses, collaborate with ML teams, investigate data issues, and shape product strategy through sophisticated statistical modeling and exploratory analysis.

Develops, evaluates, and maintains predictive and statistical models using regression, time-series analysis, and supervised and unsupervised machine learning. Integrates complex aviation, supply chain, and operational datasets; assesses data quality; identifies trends; and documents analytical processes and models. The role collaborates with stakeholders to translate requirements into data science solutions and supports repeatable data, model, and quality-management practices.

Develops algorithms, predictive analytics, automated workflows, and supervised and unsupervised machine learning models for federal health organizations. Analyzes complex structured and unstructured data, communicates findings to customers and nontechnical stakeholders, briefs military leaders, mentors teammates, and supports operational requirements. The role requires Python development, a TS/SCI clearance, travel within the continental U.S. up to 25%, and possible international deployments of up to 90 days.

Develop and enhance Palantir Foundry applications for tracking and forecasting space-launch equipment costs. Build data models, visualizations, automated workflows, and Python scripts to analyze complex datasets. Translate findings into actionable recommendations, collaborate with project teams, and produce user guides and technical documentation.

Conducts data analytics, engineering, mining, statistical analysis, predictive modeling, and intelligence-focused data visualization. Designs and optimizes data architectures, storage, access, integration, throughput, and query performance. Builds AI automation tools and prediction systems from disparate data sources, collaborates with intelligence community members, communicates complex findings, and develops concise defense intelligence products. Requires active TS/SCI clearance, U.S. citizenship, and ability to obtain a counterintelligence polygraph.

Leads advanced analytics initiatives supporting strategic planning, equity tracking, forecasting, budgeting, staffing, and resource allocation. Builds predictive models, simulations, and decision-support tools using education and enterprise data. Presents insights to executive and board-level audiences, partners with academic, operational, and finance teams, maintains reproducible workflows and data governance compliance, collaborates on data quality, and mentors analysts.

Own enterprise data inventory and translate business needs into data models, quality rules, mappings, and acceptance criteria. Partner with data and software teams on pipelines, governance, monitoring, and lifecycle management. Prepare data for agentic workflows, including semantic layers, knowledge bases, retrieval indexes, and evaluation datasets. Develop reusable analytical models, reports, dashboards, and decision-support products while monitoring data quality and communicating limitations and business impact.

Analyze big data using data mining, machine learning, and sentiment analysis techniques. Develop and apply data architecture solutions using Java open-source technologies and OpenNLP. The role requires expertise in big data analytics and at least six months of engagement.

Analyze large datasets using data architecture, Java, sentiment analysis, OpenNLP, data mining, and machine learning techniques. The role requires developing or supporting big data analytics solutions and applying machine learning methods to derive insights from complex data.