Top Data & Analytics Jobs

Evaluate enterprise data architectures, governance frameworks, integration patterns, ownership models, and data quality practices. Design target-state architectures supporting analytics and AI, define governance operating models, assess data flows, develop quality controls, and create strategic roadmaps and executive recommendations. Collaborate with business and technology stakeholders on data modernization, cloud migration, AI enablement, and enterprise data management initiatives.

Supports email and broader marketing analytics through data collection, campaign measurement, reporting, dashboards, statistical analysis, visualization, and optimization recommendations. Partners with marketing teams to translate customer and campaign data into actionable insights. Responsibilities include developing analytical models, conducting qualitative and quantitative research, documenting requirements, supporting implementation, and improving marketing processes and decision-making.

Analyze customer behavior, segmentation, clickstream data, attribution, and marketing campaign performance. Design A/B tests, apply statistical modeling using SQL, Python, and SAS, and develop automated Tableau dashboards and reporting. Provide actionable insights to improve customer acquisition, retention, engagement, and marketing ROI.

Build and maintain data engineering solutions using Python, PySpark, and big data technologies. Work with AWS services including S3, EMR, and Lambda, while supporting infrastructure through Terraform. Integrate Java, REST APIs, messaging systems, Kafka, Cassandra, Airflow, Control-M, and native schedulers. Databricks experience and familiarity with machine learning workflows are preferred.

Analyzes structured and unstructured data to identify insights, develop algorithms, create dashboards and visualizations, apply statistical and machine learning methods, integrate data systems, and advise clients on data-driven decisions. Requires R, SQL, data analytics, visualization, machine learning, a bachelor’s degree, and TS/SCI clearance with polygraph. Python, data governance, and project management are preferred.

Leads technology, data, automation, analytics, and applied AI solutions supporting investment manager research across public and private markets. Translates business requirements into scalable production systems, develops applications and dashboards, improves data quality and governance, and prototypes AI/LLM capabilities. Partners with research, investment, data, technology, operations, and vendor stakeholders while managing complex initiatives, controls, priorities, and adoption.

Build and support Python data applications, migrate systems to Azure, develop ETL pipelines, manage data lake resources, and design scalable hybrid cloud architectures. The role also involves implementing statistical and machine learning models, improving data science infrastructure, documenting solutions, and supporting analytics databases.

Leads the Category Market Insights team, developing category, consumer, competitive, and portfolio intelligence to guide commercial strategy. Synthesizes data, conducts qualitative and quantitative research, creates dashboards and research repositories, monitors competitors and trends, presents insights to executives, and translates business questions into actionable research plans. The role also develops team capabilities and applies advanced analytics, including ML and AI, to business questions.

Analytics intern supporting commercial real estate functions including analytics, FP&A, accounting, reporting, and capital markets. Responsibilities include improving processes with automation, developing KPI models and interactive dashboards, recommending machine learning applications, building predictive models, and presenting a final business case. The role is part-time, in-person, and designed for a master's student seeking practical analytics and real estate industry experience.

Leads commercial analytics and business intelligence strategy, including executive dashboards, account reporting, revenue and pricing analytics, operational KPIs, forecasting, segmentation, and predictive modeling. Partners with Sales, Finance, Marketing, Operations, and Product to improve revenue, profitability, customer retention, and marketplace performance. Presents insights to executives and customers while managing analytics teams, budgets, vendors, and BI scalability.

Principal Data Analyst responsible for investigating business challenges, managing ad hoc analytics requests, creating Tableau reports, delivering data-driven recommendations, and communicating findings to stakeholders and senior leadership. The role partners with IT and Data Engineering on data pipelines and data mart architecture, manages A/B testing projects, evaluates analytics technologies, and supports self-service reporting. Strong SQL, Tableau, data storytelling, presentation, and quantitative analysis skills are required.

Analyze structured and unstructured datasets using Python, R, and SQL; perform data cleansing, statistics, visualization, and machine learning; develop dashboards and lightweight applications; integrate data systems; guide algorithm development; and advise clients on data-driven decisions, data strategy, governance, and infrastructure.

Designs and optimizes clinical data architectures, databases, and ETL pipelines using cloud technologies. Translates business requirements into reliable data solutions, performs data quality reviews, and advises teams on data engineering best practices. Collaborates with clinicians, informaticists, engineers, and data scientists while managing concurrent projects and overseeing team tasks. Requires advanced data modeling, profiling, analysis, programming, cloud, communication, leadership, and project management capabilities.

Performs complex fraud analytics and financial modeling for money movement products. Uses SAS, SQL, Excel, Tableau, and databases to evaluate fraud risk, develop mitigation strategies, improve portfolio performance, and recommend policy changes. Leads reporting, strategy requirements, testing, and rollout activities, supports biometrics enhancements, and helps implement machine learning capabilities while communicating recommendations to senior management.

Analyzes marketing performance, ROI, attribution, segmentation, cohorts, and testing across channels. Builds dashboards and reports, ensures data quality and lineage, partners with engineers and vendors on marketing data pipelines, and translates findings into strategic recommendations. Uses AI tools, SQL, statistical analysis, and business intelligence platforms to support demand generation, lifecycle marketing, and executive decision-making while maintaining privacy and regulatory compliance.

Own Marvell’s market intelligence modeling, including TAM/SAM/SOM sizing, demand forecasts, scenarios, sensitivity analyses, and emerging-market assessments. Build reproducible analytical pipelines using statistical modeling, machine learning, Python, SQL, and Excel. Govern internal and external market data, develop living dashboards, and translate complex findings into recommendations for executives, Finance, Sales, Investor Relations, business units, and board-level stakeholders.

Designs and supports digital SHE solutions for North American manufacturing, including Power Platform applications, integrated data models, dashboards, risk maps, AI tools, and automated workflows. Translates shop-floor safety requirements into technical specifications, configures and tests applications, establishes data governance standards, and supports deployment and optimization. Advises IT, cybersecurity, legal, operations, and data-governance teams while creating documentation, training materials, and reusable solutions.

Develops business intelligence solutions, dashboards, reports, visualizations, automated reporting, and ad hoc analyses. Builds statistical, descriptive, and predictive models using machine learning and data mining techniques to generate actionable insights for financial business decisions. Ensures model documentation, reproducibility, validation, monitoring, data quality, and regulatory compliance. Collaborates with stakeholders, data scientists, engineers, and model risk teams while managing projects and communicating findings to business leaders.

Leads North American insights, analytics, and AI delivery for underwriting teams. Translates business priorities into data products and analytics solutions, drives measurable value and adoption, oversees AI-enabled transformation, partners with underwriting and enterprise Data & Analytics leadership, and develops high-performing teams. The role also promotes responsible AI governance, agile delivery, scalable solutions, and evidence-based decision-making across underwriting and portfolio management.

Leads analytics and AI initiatives for North American underwriting teams, translating business needs into analytical requirements and practical solutions. Performs hands-on analysis of complex datasets, recommends appropriate analytical methods, prototypes AI-enabled solutions, and partners with data, technology, and business teams through agile delivery. Communicates findings to technical and nontechnical stakeholders, supports model deployment and monitoring, ensures governance and explainability, and drives measurable adoption and business outcomes.

Design and build Account 360 data architecture, predictive pipelines, and automated enrichment workflows using enterprise, telemetry, and software usage data. Apply Python, R, SQL, machine learning, NLP, and process mining to deliver insights, segmentation, scenario planning, and product optimization. Collaborate with R&D, Product, Sales, Marketing, and Service teams while rapidly deploying data products, managing sprint capacity, reducing technical debt, and supporting cross-training.

Analyze customer experience and support data using SQL, Python or R, statistical modeling, predictive analytics, machine learning, NLP, and Generative AI. Develop segmentation models, KPI scorecards, and customer satisfaction insights; connect customer metrics to business outcomes and ROI; and communicate recommendations to stakeholders. The role requires 8+ years of analytics experience and supports Reality Labs customer-centric initiatives.

Conduct quantitative research for automated trading strategies by developing mathematical models, translating algorithms into code, backtesting trading signals, analyzing unconventional data, and refining monetization systems. Collaborate with senior researchers during an 11-week post-doctoral internship while applying advanced probability, statistics, machine learning, and time-series techniques to financial markets.

Lead a quantitative modeling team to develop and govern credit underwriting, pricing, collections, and portfolio risk models. Collaborate with stakeholders on acquisition pricing, marketing optimization, M&A risk assessment, and model validation. Oversee model performance, governance, and implementation to support lending strategies and maximize profitability while minimizing credit losses.

Analyze telematics, IoT, sensor, and firmware data to evaluate hardware performance, reliability, and field issues. Build dashboards and reports, identify trends and anomalies, apply statistical and machine learning techniques, validate firmware improvements, and provide actionable insights to engineering, product, data science, and customer support teams. Help develop scalable data pipelines and promote data-driven hardware and software development.