Top Data Science Jobs

Develop predictive models, machine learning algorithms, quantitative analyses, and visualizations using structured and unstructured data. Explore, clean, analyze, mine, and interpret complex datasets while collaborating with clients to address data-driven challenges. Lead solution development, communicate findings, and support informed decision-making across public and private sector projects. Travel up to 25% and maintain a Secret clearance.

Develop predictive and machine learning models, establish data strategies, source and validate data, create visualizations, and deploy scalable production solutions. Translate complex business requirements into actionable insights supporting Walmart’s e-commerce search backend. Monitor model performance, document code, collaborate cross-functionally, and guide junior team members while applying statistical analysis, NLP, and large language model expertise.

Develop machine learning, statistical, graph-based, and entity-resolution algorithms for large-scale identity verification and deceased monitoring. Build Spark/PySpark and AWS data pipelines, engineer features, analyze data quality, conduct experiments, and support model evaluation. Write Python/R and SQL for extraction and validation, investigate compliance data, create dashboards, and communicate findings to cross-functional teams.

Build and operate production agentic AI systems for Salesforce Database engineering and operations. Responsibilities include knowledge graphs and agentic memory, LLM orchestration, tool and MCP integration, evaluation infrastructure, tracing, guardrails, telemetry, workflow automation, root-cause analysis, anomaly detection, and self-remediation. The role requires hands-on full-stack delivery, distributed-systems expertise, Python and Java programming, and collaboration across database engineering teams.

Own analytics for Bevi digital experiences and websites, measure incremental impact of new flavors using experiments and quasi-experimental methods, build a flavor recommendation engine, create dashboards, and partner with Product, Design, Engineering, and Data Engineering to improve tracking, data quality, and product decisions.

Develop and deploy statistical, machine learning, forecasting, and optimization models for logistics and warehouse operations. Analyze large datasets from warehouse, transportation, ERP, and operational systems using Python and SQL. Build production-grade analytical solutions, validate and monitor model performance, and communicate insights to technical and non-technical stakeholders. Apply data science to routing, network and warehouse optimization, demand forecasting, anomaly detection, capacity planning, and segmentation.

Develop statistical models, experiments, causal inference methods, and scalable analytical workflows to generate business insights. Partner cross-functionally with Client Experience, Strategy, Marketing, Styling, Support, and Engineering teams to define metrics and improve products. Own analytical initiatives from framing through delivery, communicate executive-ready recommendations, mentor data scientists, and leverage LLMs and AI agents to accelerate analysis and workflows.

Develops reproducible Python-based data analyses and bioinformatics pipelines for life sciences experiments. Translates wet-lab workflows and scientific questions into validated analytical requirements, integrates workflows with data lake and AI systems, and builds reusable analysis modules. Collaborates with scientists, automation specialists, software engineers, and ML engineers while identifying data, LIMS, and workflow gaps and documenting methods, assumptions, and results.

Develops data science algorithms and systems to analyze structured and unstructured data, apply supervised and unsupervised AI training and NLP, manipulate data from multiple platforms, and create visualizations that help clients make informed decisions. The role requires collaboration with clients, five or more years of analytical or statistical programming experience, a Secret clearance, and a relevant bachelor's degree.

Work on Decision Sciences to design and execute statistical analyses and ML studies, wrangle large datasets, produce insights and visualizations, partner with stakeholders, and present results to improve clinical programs and ROI.

Develop and improve analytical and machine learning models supporting product quality, risk outcomes, and business decisions. Responsibilities include statistical analysis, feature development, data exploration, predictive modeling, SQL-based analysis of large datasets, algorithm research, and workflow documentation. The role collaborates with data scientists, product managers, data engineers, and analysts in a product-focused fintech environment.

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.

Lead data science initiatives focused on customer loyalty and retention. Develop causal inference models, propensity models, cohort analyses, and customer-centric KPIs. Partner with Data Engineering, Product, Marketing, Strategy, and Finance to incorporate insights into product roadmaps and financial forecasts. Standardize business logic through UDFs, oversee ambiguous end-to-end projects, and communicate findings to executives to guide strategy and challenge assumptions.

Develop predictive, machine learning, and deep learning models for Walmart’s global fraud data science team. Assess data sources, establish data strategies, validate and tune models, create visualizations, and translate complex business problems into actionable recommendations. Deploy, scale, and monitor production models while collaborating cross-functionally and mentoring team members on analytics, coding, and data storytelling.

Develop and evaluate advertising auction and monetization models, revenue and inventory forecasts, product KPIs, and rigorous A/B testing programs. Analyze customer and advertising data to identify trends, explain test outcomes, optimize revenue, and guide product strategy. Communicate concise, compelling insights through fact-based reporting and presentations while supporting large-scale, user-facing advertising products.

Build and maintain dbt and Snowflake data models supporting reporting, experimentation, and machine learning use cases. Analyze consumer payment behavior, pricing, credit, and risk dynamics; design experiments; instrument product features; and create dashboards for Product, Risk, and GTM teams. Establish data quality standards, scalable data architecture, documentation, and technical direction while mentoring analysts and engineers.

Develop, validate, maintain, and monitor machine learning and predictive models for business applications. Analyze large structured and unstructured datasets, identify trends and risks, and translate findings into actionable recommendations. Collaborate with stakeholders, model validators, compliance, risk, vendors, and technology teams throughout the model lifecycle. Support cloud modernization, model governance, regulatory compliance, and communication of technical insights to diverse audiences.

Develops cybersecurity and GRC analytics, predictive risk-scoring and machine-learning models for Department of Justice clients. The role curates datasets, supports RMF, vulnerability management and authorization workflows, builds dashboards and automated data pipelines, validates models through CI/CD and DevSecOps practices, and communicates actionable insights to ISSOs, system owners and delivery teams.

Build, validate, deploy, and monitor machine learning models for real-time fraud detection and financial crime risk. Ensure data quality, model reproducibility, and production reliability while collaborating with Risk Strategy, Product, and Engineering. Lead prototyping, establish best practices, and align teams across differing roadmaps. The role also supports model governance, modern data pipelines, and potential applications of LLMs and generative AI in risk and fraud detection.

Conduct product analytics for Super Cruise, autonomous driving, and ADAS using vehicle telemetry, fleet, safety, and customer data. Define trusted KPIs, build datasets, models, dashboards, and recurring analyses, investigate data-quality issues, apply statistical and causal-inference methods, and influence product roadmaps, launches, investments, and engineering priorities. Communicate findings, uncertainty, risks, and recommendations to technical teams, product leaders, and senior stakeholders.

Conduct statistical analysis and develop reproducible code to inform transportation policy. Manage data pipelines, data quality, governance, documentation, dictionaries, databases, and legacy-to-cloud systems. Lead complex data projects, respond to analytical requests, present findings to senior leaders and external stakeholders, collaborate across technical and non-technical teams, and mentor colleagues. Projects address congestion, fares, driver pay, vehicle electrification, and charging infrastructure.

Partners with engineering, product, and business teams to frame problems, perform exploratory analysis, build statistical and machine learning models, write production code, run experiments, analyze data, communicate findings, and support launch decisions.

Leads client-facing data science engagements for financial services customers, developing predictive risk management and business intelligence solutions across consumer lending. Performs descriptive, predictive, and prescriptive analysis using advanced statistical techniques and large-scale datasets. Programs data extraction and analysis in R, SAS, SQL, Hive, and Pig; presents recommendations to executives and customers; supports sales proposals and product adoption; mentors junior colleagues; and manages multiple initiatives with limited supervision.

Develops statistical and machine-learning models for pricing strategy, revenue optimization, demand forecasting, segmentation, and utilization analysis. Designs experiments, validates and documents models, prepares large-scale datasets, and partners with data engineering teams on scalable pipelines and deployment. Translates analytical outputs into pricing recommendations, dashboards, and decision tools while supporting governance, stakeholder presentations, and analyst enablement.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.