Top Data Science Jobs

Develop and productionize data-science products for clinical, operational, research, finance, and executive users. Frame ambiguous problems, apply predictive modeling, forecasting, experimentation, causal analysis, anomaly detection, and risk stratification. Build maintainable code, integrate models into applications and Databricks workflows, monitor performance, manage drift, and iterate after launch. Collaborate closely with domain experts, explain quantitative concepts clearly, improve reusable modeling capabilities, and use AI coding tools with appropriate skepticism.

Turn business data into decisions by identifying high-impact problems, defining company-wide metrics, conducting A/B experiments, building reliable ETL pipelines and data models, and delivering analysis, forecasting, dashboards, and modeling. Partner cross-functionally with product, engineering, research, operations, and go-to-market teams to shape priorities and ensure trustworthy, well-documented data.

Own the product modeling roadmap and end-to-end lifecycle of production machine learning models, including recommenders, routing, and agent assignment. Engineer features, design experiments, deploy iterative improvements, and define real-time model integration with engineering and product teams. Apply Bayesian methods, causal inference, and marketing measurement while translating uncertainty into business recommendations. Mentor teammates and raise data science standards through code review and documentation.

Work with clients to analyze large structured and unstructured data sets, develop and evaluate AI/ML models, mentor teammates, and deliver actionable insights to leadership. Support DoW/IC customers and translate complex data into decision-ready products.

Design and develop methods, algorithms, and automated processes to analyze unstructured, diverse, and large-scale data from disparate sources. Generate, interpret, and communicate actionable insights that support data-driven business decisions and innovation within the semiconductor technology organization.

Assists with developing, automating, and maintaining predictive and descriptive models using company data assets. Performs data mining, statistical analysis, forecasting, and ad hoc analysis to support consumer collection analytics and executive decision-making. Collaborates with IS, Finance, Operations, business leaders, peers, and vendors on data and system projects, process improvements, and decision-support solutions.

The Data Scientist will lead data science initiatives embedded in Marketing, analyzing campaigns, conversion paths, incrementality, media mix, segmentation, experimentation, and revenue modeling. Responsibilities include translating marketing problems into analytical projects, conducting rigorous mathematical and statistical analyses, collaborating on strategic initiatives, managing project execution, communicating technical findings, and mentoring junior data scientists. The role requires strong Python, SQL, causal inference, mathematical, software engineering, and communication skills.

Experienced Data Scientist supporting federal data-driven initiatives through statistical analysis, predictive modeling, machine learning, data preparation, visualization, dashboards, and actionable insights. The role collaborates with technical, business, and program stakeholders, advises on analytical methodologies, improves data quality, and contributes to modernization, automation, and emerging technology efforts.

Develop data science capabilities for government and public-sector clients by analyzing complex structured and unstructured datasets, evaluating AI and machine learning models, translating findings into actionable insights, and mentoring teammates. The role involves collaborating with clients, supporting Department of War or Intelligence Community clients, and potentially applying GEOINT, LLMs, and all-source analysis expertise.

Develop predictive models and decision frameworks, curate and validate structured and unstructured datasets, audit model deployments, monitor performance, conduct peer code reviews, and communicate analytical findings to technical and non-technical stakeholders.

Develop predictive models, forecasting tools, and machine learning solutions for logistics and readiness analytics. Conduct exploratory analysis, identify trends and risks, create dashboards and decision-support tools, validate and monitor models, and collaborate with architects and engineers to operationalize solutions. Communicate data-driven insights to technical and non-technical stakeholders.

Develops and refines machine learning and advanced analytical algorithms, manages big data platforms, performs statistical analysis and data mining, supports data visualization, and ensures reproducible workflows. The role requires an active TS/SCI clearance with polygraph and a relevant quantitative or computer science degree, with at least three years of relevant experience for bachelor’s degree holders.

Develops and refines machine learning and advanced analytical algorithms, manages Big Data platforms, performs statistical and mathematical analysis, supports data visualization, and contributes to data modeling, curation, workflow reproducibility, and software engineering across one or more mission domains.

Develops and refines machine learning and advanced analytical algorithms, manages Big Data platforms, performs statistical and mathematical analysis, supports data visualization, and contributes to data management, modeling, curation, workflow reproducibility, and software engineering across mission domains. The role requires an active TS/SCI clearance with polygraph and substantial relevant experience supported by a qualifying quantitative or computer science degree.

Develop machine learning strategy, algorithms, predictive analysis, and AI-BI solutions. Translate stakeholder needs into data product requirements, partner with engineering and QA, define acceptance criteria, test features, support bug resolution, and track usage trends. The role also contributes to product planning, process improvement, and research modeling across Vestwell’s financial savings products.

Develops advanced analytics, predictive and prescriptive models, and data-driven solutions for quality and food safety systems. Analyzes large datasets, cleans and transforms data, builds machine learning models using Azure Databricks, integrates Informatica ETL with Azure Data Factory, and presents insights through Power BI. Collaborates with stakeholders, architects, engineers, and citizen data scientists while supporting quality compliance, process improvement, and analytics strategy.

Performs data preparation, cleansing, transformation, exploratory analysis, and predictive modeling. Creates dashboards, reports, and visualizations; supports model testing, validation, and monitoring. Collaborates with project teams to address operational and business questions, documents analytical methodologies, and develops forecasting and analytical workflows. Requires an active Secret clearance, bachelor’s degree, and at least three years of relevant experience.

Build and maintain AgencyBloc’s revenue data platform in Databricks, including pipelines, automated reporting, dashboards, data models, quality checks, and reconciliation across GTM systems. Support forecasting, commissions, and predictive analytics while documenting metric definitions and governance standards. Partner with sales, client success, finance, and executives to explain findings, align revenue metrics, and deliver a governed source of truth.

Develop and deploy traditional machine learning and predictive analytics models for business problems such as churn, forecasting, and operational optimization. Build feature pipelines with data engineers, conduct statistical analysis, establish MLOps practices, monitor model performance and drift, and integrate model outputs into business intelligence dashboards. Document methodologies, support algorithm governance, and translate technical findings into actionable business insights.

Design, develop, and maintain production-ready machine learning models and AI solutions supporting student engagement, recruitment, and service delivery. Build scalable applications, data pipelines, ETL/ELT processes, and enterprise integrations using Python, Spark, and modern cloud platforms. Translate analytical outputs into practical recommendations, collaborate with technical and business stakeholders, and contribute to the University's evolving data science and AI capability.

Develop and deploy statistical and machine learning models, conduct exploratory analysis, forecasting, segmentation, experimentation, and AI initiatives. Translate business needs into analytical frameworks, KPIs, predictive insights, and recommendations. Collaborate with data engineering, BI, QA, and business teams to ensure data readiness, model integration, quality, governance, and auditability. Support revenue, marketing, financial, and operational decision-making through scalable analytical solutions.

Build and operate uplift and machine learning models that optimize personalized retention bonuses. Responsibilities include feature pipeline development, model deployment, monitoring, experimentation, causal analysis, large-scale behavioral data analysis, and communicating business impact to Marketing stakeholders. The role also involves weekly model operations, cross-functional collaboration, documentation, code review, and developing reusable modeling practices.

Develop data-driven insights and products for Salesforce’s MeshMesh networking platform. Responsibilities include product analytics, retention and behavioral analysis, data modeling, data instrumentation, algorithm deployment pipelines, self-service dashboards, and strategic advising of senior leadership. The role collaborates with Product, Infrastructure, Engineering, and data teams while promoting data-driven decision-making and improving organizational data practices.

Leads product vision, strategy, and roadmap for data science-enabled products and AI solutions. Partners with data scientists, engineers, designers, and business teams to define requirements, develop machine learning models, deploy production solutions, and monitor performance. Builds scalable Databricks and Hadoop data pipelines, Tableau dashboards, forecasting capabilities, anomaly detection, and SPC alerting. Uses SQL, Python, and R to analyze operational data, predict wafer defectivity and removal rates, improve supply-chain forecasting, and guide experimentation and investment decisions.

Develops, trains, validates, monitors, and deploys machine learning and AI models for a federal data analytics program. Applies NLP and entity extraction to structured and unstructured datasets, designs ETL processes, prepares data, creates visualizations, and documents model results. Collaborates in an Agile team and supports model automation, retraining, and performance monitoring. The role is based at DEA facilities in Arlington and Merrifield, Virginia, with occasional CONUS travel.