Top Data & Analytics Jobs

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.

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.

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.

DiffUSE seeks individuals interested in interdisciplinary roles involving computational biology, machine learning, and open scientific tooling. Responsibilities include data processing, ML research, infrastructure engineering, and community building.

As an Engineering Manager, you'll lead efforts to enhance model performance, manage team projects, and support professional growth in a dynamic environment.

Agap Technologies, Inc. offers online Machine Learning training with real-time instruction, immediate support, interview training, and OPT guidance for enrolled trainees.

Design and implement scalable Snowflake-based data architectures on cloud (Azure/AWS). Build and optimize ETL using SnowSQL and Spark, tune performance, migrate data systems, document designs, collaborate with stakeholders, and mentor engineers while incorporating agentic AI into the data engineering SDLC.

Lead implementation of metadata governance and lineage for structured and unstructured data in cloud environments. Ensure metadata synchronization across catalogs, ESRI/Kubernetes, and enterprise repositories. Apply OpenLineage/Apache Atlas or similar tools, enforce DoD/NSG geospatial metadata policies, and advise clients on metadata strategy and integration.

Design, build, and deliver Power BI dashboards and analytics solutions using AWS, SQL, and Python. Develop data models, ETL pipelines, and analytics logic; incorporate AI/ML where appropriate. Partner with stakeholders, IT, and vendors to gather requirements, test solutions, and translate insights into business decisions to improve customer experience for a utility program.

Lead global pricing strategy and modernization for wholesale and specialty insurance, driving analytics, AI/ML integration, pricing model governance, catastrophe and transaction-level pricing support, and talent development to improve underwriting performance and return on capital.

Conduct execution-quality research across global markets: model market impact and slippage, design A/B tests and causal analyses, build optimization and simulation tools, and deliver analytics and decision support to traders and PMs for improved order routing and execution.

Implement, install and administer an advanced analytics platform and workspaces (SAS, Tableau, R, ML). Ensure IT policy compliance and service levels, support connectivity to data sources (Hadoop, Oracle, SQL Server), perform capacity planning, performance tuning, document procedures, troubleshoot issues, coordinate implementation with architects and service providers, and automate platform administration with scripting.

The role involves leading data science initiatives, managing large datasets, developing predictive models, and building a high-performing analytics team.

Lead development of advanced analytical and machine learning models to optimize forecasting, inventory placement, and supply chain flows. Automate reports, analyze large datasets, deliver insights to leaders, drive cross-functional initiatives, mentor junior analysts, and recommend process improvements to boost operational performance.

The Quantitative Researcher will focus on creating predictive alpha signals, applying ML techniques, and optimizing execution in cash equities. Close collaboration with teams to maximize P&L opportunities is key.