Top Data Science Jobs

Develop and model data, extract and manipulate datasets, analyze network and operating system logs, create workflows and predictive models, and build dashboards that support data-driven decisions. The role requires Splunk and Splunk SOAR expertise while supporting a classified Intelligence Community customer. Candidates will work with complex network environments and security enclaves, applying statistical analysis and cybersecurity knowledge to improve forecasting and decision-making.

Analyze large payments datasets to support ChaseNet pricing, profitability, portfolio optimization, and merchant economics. Build reporting, analytical models, and pricing frameworks using SQL and Python. Partner with product, finance, pricing, and relationship teams to develop actionable recommendations. Support analytics governance, automation, quality control, and documentation while communicating findings through narratives, visualizations, and presentations. Contribute to AI and emerging technology initiatives.

Develop machine learning and statistical models using large-scale structured and textual data to support Small Business Bank marketing and deposit strategy. Partner with software engineers, product managers, and data scientists to deliver data-driven products. Build account-level lifetime value and NPV models, translate technical findings into business outcomes, and manage model development from design through implementation.

Develop and deploy scalable optimization, simulation, and machine learning models for banking decision systems. Translate complex business challenges into mathematical problems, build production-grade algorithms using Python and optimization solvers, design experiments, and integrate modeling solutions with software systems. Collaborate with Product, Engineering, and Business teams to improve customer experiences, risk policies, and operational decision-making.

Lead end-to-end data science initiatives for economic and financial analysis. Responsibilities include curating complex datasets, developing statistical, machine learning, econometric, and generative AI solutions, validating data sources, identifying economic trends, generating forecasts, and communicating insights to technical and executive audiences. The role partners with economists, strategists, product owners, and data scientists while advancing LLM and GenAI adoption.

Designs enterprise technical solutions spanning applications, integrations, data, infrastructure, and cloud platforms. Leads technical stakeholders and implementation teams while resolving risks. Defines data requirements, collection methods, and quality standards; builds systems for analysis and insight generation; improves models and algorithms; and ensures security, privacy, and compliance. The role also applies NLP, generative AI, and large language models to scalable, knowledge-driven solutions.

Build and deploy intelligent agents, automation solutions, and internal tools using Copilot, generative AI, statistical analysis, and machine learning. Prepare data pipelines, analyze structured and unstructured datasets, integrate solutions with business platforms, and continuously monitor and improve deployed agents. Partner with stakeholders to identify automation opportunities, develop data-driven insights, and communicate recommendations through visualization and storytelling.

Leads the full analytics lifecycle for advertising sales, including data preparation, exploratory analysis, predictive modeling, validation, productionalization, interpretation, and recommendations. Develops scalable data-driven solutions using statistical and machine learning methods, presents findings to varied audiences, mentors junior staff, contributes to strategic direction, and manages project execution. Requires advanced programming, SQL, cloud, big-data, forecasting, optimization, and AI/ML expertise.

Leads the full analytics lifecycle for Spectrum Reach’s advertising sales business, including data preparation, exploratory analysis, predictive modeling, validation, productionization, forecasting, and interpretation. Develops scalable analytics products, synthesizes recommendations, presents findings to diverse audiences, manages project execution, and mentors junior data professionals. Requires advanced statistical, machine learning, programming, cloud, and big-data expertise.

Leads Responsible AI data science initiatives by developing and validating AI testing protocols, automating analyses with Python, identifying PII and PHI vulnerabilities, and designing red-team scenarios for conversational AI. The role documents responsible AI practices, supports governance and regulatory compliance, communicates findings to legal, compliance, and technical stakeholders, and mentors colleagues. It requires expertise across traditional machine learning, generative AI, agentic techniques, AI ethics, fairness, and the full AI lifecycle.

Develop and productionize AI, machine learning, and statistical models for workforce demand forecasting, capacity optimization, scheduling, and real-time decision-making. Responsibilities include framing ambiguous business problems, selecting theoretically grounded modeling approaches, collaborating with stakeholders, ensuring solution robustness, communicating data-driven recommendations, and supporting complex cross-functional projects. Advanced work may involve time series, operations research, NLP, neural networks, and cloud technologies.

Designs and maintains reliable ETL/ELT pipelines, data lakehouse architectures, schemas, scheduling, monitoring, and data quality frameworks in an air-gapped environment. Partners with engineering and testing stakeholders to deliver technical data solutions, integrates APIs with Jira and Confluence, supports Ubuntu infrastructure, and uses Python, PySpark, PyIceberg, GitLab, Docker, PostgreSQL, and CI/CD practices.

Conduct applied AI research and develop automation, knowledge management, semantic search, retrieval, and reasoning systems. Build and evaluate proof-of-concepts, create frameworks for assessing AI risks and business impact, and collaborate with engineering teams to productionize successful concepts. The role explores agentic, causal, explainable, neuro-symbolic, and knowledge-enhanced AI, while contributing to publications and patents across financial services, fraud, risk, insurance, and marketing applications.

Leads end-to-end data science initiatives involving machine learning, statistical modeling, causal inference, sequence analysis, and scalable insight generation. Builds and deploys production-ready solutions using Python, SQL, Spark, Databricks, and cloud platforms. Provides technical leadership and mentorship, establishes model validation and governance practices, manages cross-functional initiatives, and communicates analytical findings to technical and business stakeholders. The role supports agricultural and digital product applications.

Develop and maintain ETL workflows and data pipelines using Python, Apache NiFi, shell scripts, and APIs. Ingest, transform, validate, and deliver data for mission applications, analytics, and data science. Support Kubernetes workloads, CI/CD, automated testing, Linux administration, monitoring, troubleshooting, and data quality improvements. Collaborate with software, platform, mission, and data science teams in secure environments while documenting workflows and participating in agile delivery.

Analyze large datasets using quantitative methods, data mining, statistical modeling, and AI tools to generate actionable product and business insights. The role designs and tracks product metrics, builds dashboards and reports, identifies trends and opportunities, and supports data-driven decisions. Responsibilities include collaborating with product managers, engineers, marketers, and designers, applying statistical techniques such as A/B testing and causal analysis, and independently driving projects to completion.

Research and develop hypotheses using diverse real-world datasets, apply advanced methodologies to extract meaningful signals, and rigorously test theories with engineers and business stakeholders. The role involves literature reviews, quantitative data analysis, clear communication of complex ideas, and ongoing engagement with the academic research community.

Develop and operationalize advanced analytics for a large-scale government data platform. Responsibilities include statistical modeling, machine learning, predictive analytics, data exploration, model validation, experiment design, and integrating analytical capabilities into production workflows. The role collaborates with engineers, analysts, and government stakeholders to translate mission requirements into scalable solutions and communicate findings to technical and executive audiences.

Develop and operationalize statistical models, machine learning algorithms, and predictive analytics for a large-scale government data platform. Analyze complex datasets, identify patterns and anomalies, validate models, and integrate analytical capabilities into production workflows. Collaborate with engineers, developers, analysts, and government stakeholders to translate mission needs into scalable solutions and communicate findings to technical and senior audiences. The role requires an active TS/SCI clearance and is primarily onsite in Herndon, Virginia, or Westminster, Colorado.

Develop product analytics and data science solutions for small-business insurance and banking products. Apply statistical analysis, predictive modeling, experimentation, AI, and customer journey analytics to improve product performance, engagement, lead generation, and client outcomes. Establish KPI and measurement frameworks, analyze data using Python and SQL, communicate insights to senior stakeholders, and identify scalable AI and agentic use cases in a regulated environment.

Lead data science for SEO and AEO-driven patient growth. Develop measurement and experimentation strategies, combine internal and external data sources, evaluate causal impact, answer product and business questions, and automate analysis using AI agents and workflows. Partner with product, marketing, engineering, content, and design to improve organic search and AI visibility. Establish measurement standards and communicate insights, limitations, and recommendations to executives and cross-functional teams.

Leads complex data science and AI initiatives, developing and deploying machine-learning solutions centered on LLMs, agentic workflows, and RAG systems. Translates stakeholder needs into technical roadmaps, mentors analysts and data scientists, manages project budgets and schedules, ensures quality, and designs reliable AI applications using embeddings, vector databases, retrieval systems, and AI workflow platforms.

Develops data integration workflows, dashboards, statistical and predictive models for biotherapeutics process development. Analyzes complex scientific and process datasets, identifies trends and anomalies, supports process improvement, and communicates findings to technical teams. The role also troubleshoots data and software issues, trains team members, collaborates externally, and contributes to project deliverables and modeling initiatives.

Develop machine learning and statistical models for Business Cards marketing, including design, training, evaluation, validation, and implementation. Analyze large numeric and textual datasets using Python, cloud computing, and big-data technologies. Partner with data scientists, engineers, product managers, analysts, and marketers to translate insights into personalized campaigns and measurable business outcomes.

Develops data science and machine learning solutions for auto finance products. Analyzes digital customer behavior, builds predictive models, evaluates A/B experiments, and creates measurement frameworks and KPIs to improve conversion and engagement. Partners with Product, Technology, Design, Risk, and business leaders to translate complex questions into actionable insights, communicate recommendations, and support strategic decisions. May also build conversational AI solutions and integrate machine learning services into applications.