Top Data & Analytics Jobs

Build reusable, analytics-ready telematics data products on Azure Databricks. Design canonical datasets, standardize metrics and business logic, optimize performance and cost, enable self-service analytics, and support AI/ML use cases for enterprise telemetry and connected-vehicle data.

Join Aladdin Financial Engineering as a quantitative modeler to research, develop, and deploy financial and portfolio risk models. Lead model governance workstreams, collaborate with researchers and engineers globally, present analytics to stakeholders and clients, and contribute to model validation, backtesting, and production deployment.

Lead control and risk management for Data & Analytics products and platforms. Ensure adherence to the CORE program, embed AI/ML and data governance controls, manage Risk Assessment Structures, support control testing and issue remediation, develop control documentation, partner with Legal/Compliance/OpRisk/Audit, and support regulatory responses while leading and advising stakeholders.

Lead Data Architect responsible for defining and evolving data architecture and standards across 100+ applications, driving adoption of strategic data lake platforms, designing target-state architectures and data exchange patterns, mentoring junior architects, and ensuring governance, data quality, and cloud-based database best practices.

As a Quantitative Research Engineer, design, develop, and implement software for automated trading systems using advanced statistical techniques. Collaborate with researchers and prioritize business needs.

Lead design and delivery of reusable, AI-ready data products and interoperable data-engineering artifacts for retail-banking portfolios. Manage data product lifecycles, ensure data integrity and governance, create self-service analytics, and align technology and business teams in a matrixed organization.

Lead enterprise data engineering strategy and roadmap to build scalable cloud-native data platforms. Oversee design and modernization of data architectures, pipelines, and integration. Ensure data security, privacy, and governance, incorporate AI/ML, and drive data democratization. Lead global teams, collaborate with business and technology leaders, and deliver enterprise-wide data capabilities to enable analytics, innovation, and business growth.

Design, harden, and scale Snowflake and AWS data platforms, build dimensional data models and resilient Airflow pipelines, optimize complex SQL, develop dbt semantic layers, deploy ML models in AWS, integrate AI/LLMs into workflows, and build data APIs (FastAPI/Flask) to enable enterprise analytics and operational ROI.

As Head of Investment Data, you will define and drive data strategy, governance, and operations to enhance investment decision-making and client outcomes.

Lead architecture for data, reporting, and analytics product family: set target-state designs, guide technology evaluation and AI-enabled integrations, produce and review production code, enforce governance, resiliency, security, and auditability, and provide technical leadership across teams and vendors.

Designs and owns enterprise data quality platforms and tooling, translating governance requirements into scalable technical architectures. Assesses platforms and integration patterns (batch, event-driven, API), documents architecture decisions, ensures non-functional requirements, and partners with product and platform teams to deliver governed, auditable data quality solutions.

Build and maintain analytics data systems and pipelines that capture, transform, and integrate product usage data at scale. Deliver accurate insights and visualizations to inform product decisions, collaborate cross-functionally, and apply AI tools to improve analytics and workflows.

Lead data science projects to optimize drug development using AI. Develop models for patient selection and clinical trial evaluations while collaborating with cross-functional teams to drive insights.

Lead and architect high-value healthcare analytics engagements, build reusable analytic frameworks (DBT), partner with product and engineering, mentor analysts, ensure analytic quality, and represent Arcadia externally to drive population health and value-based care outcomes.

Develop and implement software solutions for automated trading systems, working with quantitative researchers on complex financial challenges.

Design, develop, and deploy software solutions for automated trading systems while collaborating with researchers on technology priorities.

As a PhD intern, you'll design, develop, and deploy software solutions for automated trading, collaborating with the Quantitative Research team.

Develop and validate commercial credit risk models (rating, loss forecasting, stress testing, pricing). Collaborate with business, data infrastructure, and governance teams to deploy, monitor, and document cloud-based model solutions and derive business insights from large datasets.

Lead and scale the data engineering organization to build data products and architecture that power GTM (marketing and sales) strategy. Define technical strategy, enable reporting, analytics, data science, ML/AI workloads, and partner with IT, product, analytics, and GTM stakeholders to deliver production-grade data platforms and services.

Develop and deploy advanced statistical and ML models on large-scale alternative datasets to generate investable signals, partner with investment teams, evaluate data quality, and drive novel data-driven research with commercial impact.

Curate, cleanse, validate, and maintain large US equity and options datasets; integrate fundamental and alternative data; build data pipelines for research and trading; perform statistical and ML analysis to identify market patterns; document data provenance and collaborate with engineers and traders to deploy production-grade models.

Lead data management for marketing execution: design targeting and measurement strategies, build repeatable datasets, automate reporting, implement monitoring and quality checks, enable responsible AI-supported workflows, and quantify value from automation and audience improvements.

The EO Imagery Analyst will provide geospatial and imagery analysis to improve data operations for ML algorithm testing, working with EO imagery products and metadata.

The Geospatial SAR Imagery Analyst Scientist analyzes SAR data to improve machine learning algorithms, curating imagery for data sets and recommending strategies for data gaps.

The EO Imagery Geospatial Analyst/Scientist provides geospatial expertise, conducts assessments, analyzes data formats, and supports ML algorithm testing.