Top Data & Analytics Jobs

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.

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.

Develops business intelligence reporting, financial data pipelines, scalable data models, and AI-driven applications for Product Finance. Uses SQL, Python, and BI tools to analyze cloud spending, automate financial workflows, identify cost anomalies, forecast spend, and generate executive insights. Partners with Finance, Engineering, and Data teams to deploy scalable analytical and generative AI solutions.

Build a canonical entity and knowledge graph for identity resolution, permissions, compliance, and agent authorization. Develop production data pipelines across internal systems, graph-based correlation, ML matching, schemas, APIs, provenance, bitemporal models, drift detection, and delegation credentials. Partner with Finance and Compliance on classification pilots while enforcing privacy, regulatory compliance, jurisdictional partitioning, and auditability.

Leads Lexia’s business analytics and data strategy function, managing analysts and overseeing data engineering alignment. Owns BI strategy, Tableau governance, data governance, self-service dashboards, AI-enabled analytics, metric definitions, and data quality. Partners with Sales, Marketing, Customer Success, executives, and analytics engineering to translate business needs into insights, KPIs, models, and actionable recommendations. Manages analytics tools and vendors while ensuring responsible, secure, and effective use of enterprise data.

Designs and maintains data architecture, ETL/ELT pipelines, data warehouses, lakes, and workflow automation. Ensures data quality, governance, security, availability, and performance while supporting reporting, analytics, dashboards, and machine learning. Translates business needs into technical data models, monitors data systems, and collaborates with Technology, Analytics, and business stakeholders. The role is based in the administrative office with work-from-home eligibility; travel is not required.

Leads analytics for B2B sales, partnerships, revenue operations, and marketing teams. Responsibilities include forecasting, opportunity sizing, partner performance analysis, enterprise market intelligence, RFP analytics, lead scoring, funnel and lifecycle optimization, predictive modeling, experimentation, and executive insight generation. The role partners with Data Science and Engineering teams to improve revenue growth, onboarding, retention, campaign effectiveness, and sales performance across enterprise and SMB channels.

Supports production planning and digital transformation by analyzing manufacturing, inventory, scheduling, and production-performance data. Builds dashboards, reports, and visualizations; documents workflows; identifies automation and AI opportunities; assists with analytics and digital-tool pilots; validates data; researches emerging technologies; and presents recommendations to operations, planning, and leadership stakeholders in a manufacturing environment.

Build and maintain production-grade data models while migrating workflows from Alteryx to dbt. Manage Dagster or Airflow pipelines in Redshift, create Tableau dashboards, and translate technical data into RevOps insights. Partner with Sales, Marketing, and Customer Experience leaders on KPI reporting, ABM, and customer health scoring. Explore AI, LLM, predictive analytics, and SageMaker machine learning applications including churn prediction and lead scoring.

Build and maintain dbt data models, migrate Alteryx workflows, and manage Dagster or Airflow pipelines in Redshift. Partner with Revenue Operations stakeholders to translate data into business insights, dashboards, and KPI visibility using Tableau. Apply AI, LLMs, and machine learning through SageMaker to support predictive use cases such as churn prediction and lead scoring.

Build and maintain dbt data models while migrating workflows from Alteryx, managing Dagster or Airflow pipelines, and supporting Redshift analytics. Partner with RevOps stakeholders to translate data into business insights, dashboards, KPIs, ABM initiatives, and customer health scoring. Explore LLMs, predictive analytics, and SageMaker machine learning for use cases such as churn prediction and lead scoring.

Build and migrate production data models from Alteryx to dbt, manage Redshift pipelines with Dagster or Airflow, and deliver stakeholder-facing Tableau insights for Revenue Operations. Translate technical data into business impact while supporting ABM, customer health scoring, predictive analytics, churn prediction, lead scoring, and AI/LLM initiatives using machine learning and Amazon SageMaker.

The Director leads the Data & Analytics department, setting strategy, architecture, governance, and a multi-year roadmap. Responsibilities include overseeing data pipelines, analytics, visualization, AI/ML applications, data quality, risk controls, and self-service solutions. The role manages stakeholder engagement, executive reporting, vendor partnerships, and cross-functional delivery while recruiting, developing, and retaining a high-performing multidisciplinary team.

Summer Analyst internship for students pursuing a bachelor’s or graduate degree. Quantitative strategists apply mathematics, programming, logical reasoning, research, and analytical methods to construct quantitative models supporting risk, financial controls, compliance, treasury, and other core financial-market processes. Interns receive training, work on real responsibilities, and collaborate with experts in quantitative finance and engineering.