Top Data Science Jobs

Perform statistical and operational analyses, assess and improve data quality, use SQL and GIS/geospatial data to support reporting, evaluate program outcomes, build public-facing reporting tools, and coordinate data sharing and communications with stakeholders.

The Senior Data Scientist will lead data analytics projects, interface with clients, and develop predictive models for business intelligence using various data tools.

As a Senior Data Scientist, you'll design data-driven solutions, analyze performance, collaborate across teams, and communicate insights to drive decision-making.

Lead risk-focused data science work by analyzing transactions and user behavior, developing automated risk controls and decisioning systems, advising leadership on risk strategy, and partnering cross-functionally to improve risk processes and scalability in a fast-paced fintech environment.

As a Lead Data Scientist, you will lead a team to develop AI solutions, collaborate with cross-functional teams, design predictive models, and ensure data quality in machine learning pipelines.

The Lead Data Scientist will oversee end-to-end data science projects, develop statistical models and machine learning algorithms, and collaborate across teams to deliver impactful data solutions.

Lead causal inference and marketing mix modeling to measure and optimize marketing investments. Define problems with stakeholders, build statistical pipelines and production models, design and analyze experiments, deploy solutions, and support automated campaigns via science on-call rotation to drive Growth outcomes.

Lead causal inference and marketing mix modeling to measure and optimize marketing investments. Drive end-to-end data science work for Growth: define problems with stakeholders, build statistical pipelines, write production code, design and analyze experiments, deploy production models, and participate in on-call rotations to ensure automated campaigns run smoothly.

Lead measurement and optimization of marketing investments using causal inference and MMM. Deliver end-to-end data science solutions for Growth: define problems, build statistical pipelines, productionize models, design and analyze experiments, own roadmaps, and support automated campaigns via on-call rotation.

Use statistical modeling and analytical frameworks to shape data acquisition, validation, and ML training strategies for AD/ADAS. Develop metrics to characterize data distribution and diversity, perform ambiguous problem analysis to inform decisions, communicate findings to stakeholders, embed with engineering teams for domain-specific recommendations, and mentor data science engineers to drive best practices.

Design and implement forecasting models and algorithms for pricing and promotions, build features in the forecasting platform, clean and validate data, produce visualizations, write maintainable code, and collaborate with subject-matter experts and clients to translate data into retail business solutions.

Provide technical leadership for enterprise data analytics: design analytical architectures, implement systems and databases, apply statistical and engineering methods, build and optimize SQL analytical structures, guide BI platform selection, ensure data quality and performance, perform tradeoff analyses, collaborate cross-functionally, and communicate findings to non-technical stakeholders.

Lead applied data science for customer experience by mining structured and unstructured data, designing experiments, building predictive models, and operationalizing solutions. Advance AI/LLM capabilities, mentor data scientists, partner with BI and data engineering to scale analytics, and communicate insights to influence business decisions.

Lead individual-contributor data science work to build predictive models, measurement frameworks, and AI-powered analytics. Partner with Product, Engineering, and Operations to translate insights into product strategy, define success metrics, automate analyses with agentic workflows, apply causal inference and experimentation, communicate findings to executives, and mentor other data scientists.

Lead end-to-end on-chain data ingestion, attribution, and composability decoding for DeFi surveillance. Build and integrate wallet-clustering feeds, trace funds across chains and bridges, co-develop optimistic-oracle (UMA) surveillance R&D, and deliver versioned on-chain feature tables for detection and validation systems.

Lead advanced analytics and predictive modeling to optimize marketing channels. Design, develop, and deploy complex models and algorithms, test hypotheses, communicate insights to senior leaders, mentor team members, and drive best practices in model lifecycle and ethical AI.

Lead design and scaling of yield, pricing, and matching models; analyse large-scale trading and behavioral data; define data science strategy for optimisation and forecasting; partner with commercial, engineering and product teams; mentor senior/mid-level data scientists; communicate technical findings to non-technical stakeholders and ensure robust production deployment.

Use client platform data to identify trends and actionable insights, define analytical requirements, ensure data quality and governance, and develop statistical, data-mining, and predictive models. Create reports and dashboards, integrate multi-source data, validate accuracy, document processes, collaborate to optimize data systems, implement reusable analytics components, evaluate tools, and train users to support mission-driven, data-informed decision-making.

Lead analysis of large datasets using Databricks to identify trends, model customer retention/churn, create visualizations and dashboards, and integrate AI-driven solutions to support decision-making for consumer and retail analytics.

Lead data science research and develop scalable ML models, audience segmentation, propensity and recommender systems for marketing and eCommerce. Cleanse and integrate large disparate datasets, apply causal inference and A/B testing, define KPIs, ensure data governance and privacy, and communicate insights to product, sales, and marketing stakeholders to drive revenue and product improvements.

Lead end-to-end data science initiatives across pricing, dispatch, ETA, routing, and forecasting. Turn ambiguous logistics problems into production ML solutions, build data pipelines, partner with engineering to deploy models, establish evaluation and A/B testing, and work directly with customers and leadership to measure business impact.

As a Lead Data Scientist at Oliver Wyman, you will manage analytics projects, develop models, and engage with global clients to solve business challenges using advanced data science methodologies.

The Senior Data Scientist will manage and deliver analytics projects, engaging with clients to solve business problems using data science and machine learning techniques.

Analyze large-scale mobile user and app datasets to inform product and ML strategies, collaborate cross-functionally, build dashboards and processes, and present statistical analyses to improve platform performance.

Lead end-to-end data science efforts across pricing, dispatch, carrier selection, ETA prediction, routing, and forecasting. Own discovery through production, build models and pipelines with Python/SQL/Snowflake, partner with engineering to serve models, establish evaluation/monitoring/A-B testing, and measure impact on cost, reliability, and delivery performance while working closely with product, operations, and customers.