Top Data & Analytics Jobs

Lead client-facing CRM analytics and insights across segmentation, campaign measurement, predictive modeling, and AI-driven marketing. Translate goals into metrics and analytical plans, manage end-to-end delivery, ensure data pipeline integrity with engineering partners, and mentor junior analysts while driving scalable, repeatable analytics solutions and dashboards.

The Data & Analytics team at Surescripts transforms healthcare data into actionable insights, collaborating with various stakeholders to support critical decisions across the healthcare ecosystem.

Design, implement, and optimize marketplace risk strategies to mitigate buyer/seller/account abuse and payment fraud. Use large-scale analytics, experimentation, and partnership with Data Science and engineering to operationalize models, monitor KPIs, identify fraud trends, and balance fraud prevention with user experience. Mentor analysts and lead cross-functional initiatives to protect platform integrity.

Produce financial and market intelligence, build statistical and econometric models, develop dashboards and automated reports, perform A/B testing and ML-based forecasting, optimize programmatic ad campaigns, integrate third-party data, provide customized measurement products, and support client analyses and strategic planning.

Design, build, and maintain large-scale ETL/ELT data pipelines and cloud data architectures (AWS/Azure). Optimize Spark/Databricks/Synapse jobs, orchestrate workflows with Airflow, manage SQL/NoSQL databases, implement governance/security, create dashboards, integrate APIs, support ML model pipelines, and deploy using Docker/DevOps practices while monitoring pipeline health.

Build and maintain BI dashboards and reports, partner with business teams to translate requirements into SQL datasets, ensure governed data and visualization standards, manage BI tool lifecycle, and apply advanced analytics to uncover insights from complex data.

Design and implement data integration and data warehouse solutions using big data technologies. Build cost-efficient, performant cloud data pipelines, apply data modeling and machine learning knowledge, and implement real-time streaming and aggregation patterns.

Build data foundations and end-to-end pipelines, research and implement quantitative pricing, market-making, and risk models for prediction markets, model cross-market dependencies and parlays, develop backtesting/simulation frameworks, monitor model performance and collaborate closely with traders to improve pricing and trading outcomes.

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

Lead development, validation, and operationalization of P&C actuarial models and workflows. Convert actuarial requirements into production-ready code, leverage cloud/AI tools, validate data pipelines, produce client-ready reporting, and liaise between actuarial SMEs and engineering teams while contributing to practice tooling and business development.

Lead development of statistical and simulation-based models to evaluate alternative datasets, assess portfolio risk, and optimize private markets investment strategies. Integrate and clean large, imperfect datasets; produce proprietary research, client-ready insights, and presentations. Partner with engineering, data, and validation teams to deploy analytics, maintain applications, and support investment, marketing, and client-use cases while managing multiple projects and stakeholder relationships.

Lead design and build of a cloud-based big data analytics platform: implement analytics infrastructure, integrate streaming and batch data, develop distributed processing pipelines, manage predictive analytics models, and apply machine learning and Bayesian methods. Work involves Scala on OS X and AWS, Java and Clojure, Kafka/Samza, NoSQL, and following TDD, Agile, CI/CD practices.

Develop and optimize high-volume Teradata data warehouse applications: design/architect solutions, model data, write and tune SQL, use Python scripting; ML and Hadoop experience a plus.

Design and architect data solutions and large-scale Java/J2EE systems. Define extensibility, scalability, availability and security requirements. Document current and target state, gather functional and non-functional requirements, recommend process optimizations, and consider impacts of existing infrastructure and networks.

Design and deliver full lifecycle enterprise and big-data architectures, translate complex functional and technical requirements into detailed designs, lead presales and client presentations, perform advanced analytics and ML modeling (logistic/linear regression, random forest, neural networks, time series), define data requirements and analytical plans, and collaborate with cross-functional teams and stakeholders.

Design and implement Tableau BI solutions; work with Big Data stack (Hive, Pig, MapR, Spark); develop ETL (Datastage, Unix scripting); apply Scala/Python for machine learning; leverage AMEX domain expertise.

Analyze A/B experiments and large datasets to determine why experiments performed well or poorly. Communicate results to business partners, validate and improve data-driven decision processes, build key metrics and modeling reports, and collaborate with business and engineering stakeholders.

The Research Engineer will design and build evaluation systems to measure data quality for Firecrawl's web data extraction, creating metrics and benchmarks, and collaborating on model improvements.

The Senior Product Manager will lead strategy and execution for data products within the Global Data & Analytics team, focusing on analytics, ML, and AI use cases while partnering with engineering and stakeholders to deliver scalable solutions.

The Data Scientist role involves analyzing payment and subscription experiences, applying data science methods, and collaborating with cross-functional teams to improve player experiences and business decisions.

Lead design, build, and operation of data architecture, pipelines, and semantic layers. Manage sprint planning, DevOps metrics, data quality and monitoring, and MLOps enablement. Partner with stakeholders, coach engineers, track project plans, and ensure data availability, accuracy, and documentation.

Lead enterprise data governance strategy, policies, and operating model. Define data standards, quality metrics, and lifecycle management. Partner with data stewards, IT, legal, and executives to enforce privacy, compliance, and responsible AI practices. Provide technical leadership for data architecture, integration impact assessment, infrastructure tuning, and escalation support while driving stakeholder alignment and measurement for IT steering.

Design and optimize hyperscale data pipelines and ETL for multi-terabyte hardware telemetry. Build ML-driven failure prediction and anomaly detection, create dashboards and real-time telemetry visualizations, lead hardware characterization and A/B studies, perform root cause analysis, and collaborate with cross-functional teams to improve hardware performance, reliability, and energy efficiency for AI platforms.

Lead design, build, and administer Azure Databricks data pipelines and platform infrastructure. Ensure catalog architecture, data quality, security/governance (Unity Catalog), stakeholder engagement, Power BI administration, cost management, and delivery of complex projects using modern ETL, streaming, and ML capabilities.

The Head of Data will define and execute Crosby's data strategy, build data systems, enhance AI performance, drive analytics, and lead the data team.