Top Data Science Jobs

Leads the design, development, deployment, and validation of advanced analytics and machine learning solutions. Partners with clients and stakeholders to translate business objectives into analytical strategies, analyzes complex datasets, communicates insights, mentors technical teams, establishes model governance practices, and supports project planning, solution architecture, delivery, and business development.

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.

Design and lead fairness and bias evaluations for AI-assisted People systems across hiring, performance, promotion, development, and workforce planning. Conduct algorithmic audits, subgroup and intersectional analyses, validation studies, counterfactual and generative AI testing, and human-AI workflow evaluations. Build scalable testing, monitoring, documentation, and reporting infrastructure. Partner with engineering, People, legal, privacy, and security stakeholders to recommend mitigations and communicate evidence, risks, limitations, and uncertainty to senior leaders.

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.

Leads the design, development, and production deployment of large-scale operations research and optimization solutions for pricing, inventory, supply chain, and marketing decisions. Builds LP, MILP, and MINLP models; uses optimization solvers; develops APIs, data pipelines, monitoring, and automation; evaluates model performance; and partners with stakeholders to translate business problems into actionable decision systems.

Conduct data science, data characterization, technology assessment, operational utility evaluation, testing, and demonstrations for national security programs. Document and communicate findings to senior management through written, verbal, and multimedia methods. The role requires intelligence community experience, defense intelligence and all-source analysis knowledge, text exploitation, advanced statistical and modeling tools, and a TS/SCI with Full Scope Polygraph clearance.

Develop and maintain data analytics solutions, predictive models, and machine learning algorithms supporting LNG production efficiency, safety, and downtime reduction. Analyze data lake and warehouse data, define ETL and architecture requirements, document solutions, and improve data science lifecycle practices. Partner with stakeholders and IT leadership, contribute to governance and testing, design scalable data platforms, and mentor junior data scientists and analysts.

Designs and maintains data-driven risk management solutions for PayPal. Responsibilities include predictive modeling, data analysis, forecasting, risk rule development, monitoring solution efficacy, managing conversion and loss targets, defining data and platform requirements, and collaborating with product, technology, business, and global stakeholder teams. The role presents risk strategies to senior management and external partners while balancing financial performance, customer experience, and risk objectives.

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.

Develop data science capabilities by analyzing complex structured and unstructured data, evaluating AI/ML models, translating findings into actionable insights, collaborating with clients, and mentoring teammates. The role supports defense and intelligence clients and may involve GEOINT, LLMs, and all-source analysis.

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.

Own end-to-end machine learning projects for travel and B2B optimization, including look-to-book prediction, ranking, recommendation systems, cache freshness, pricing, and multi-objective optimization. Build and productionize models using large-scale data, collaborate with data engineers and analysts, define experiments, and deliver measurable business impact through robust Python pipelines and services.

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.

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

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.

Designs, builds, evaluates, and scales generative AI tools, agents, workflows, and analytical models for Customer Experience. Responsibilities include prototyping AI capabilities, developing production-ready solutions, supporting enablement and training, communicating insights, managing projects, mentoring analysts, and partnering with business stakeholders. The role requires advanced statistical modeling, GenAI expertise, data storytelling, and strong communication skills.

Develop advanced analytics, statistical models, machine learning, and GenAI solutions for Microsoft’s sales, marketing, and support platforms. Translate business needs into measurable problems, create implementation plans, write reusable modeling code, and deliver statistically reliable results. Lead GenAI solution evaluations, diagnose root causes, and recommend fine-tuning or reinforcement-learning improvements. Collaborate with data scientists, engineers, product managers, and stakeholders to drive product insights and AI transformation.