Top Data & Analytics Jobs

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.

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.

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.

Lead thought leadership by turning advanced analytics, internal data, and market intelligence into data-backed narratives, reports, and sales enablement. Use Python and SQL to analyze datasets, validate hypotheses, and surface industry insights on credit lifecycle, fraud, and regulatory trends. Collaborate with Data Science, Product, Sales, and Marketing, support strategic deals with analytical proof points, and present insights externally to senior audiences.

Support design, development, testing, and implementation of automation and AI-enabled tools. Assist with data collection, analysis, visualization, and reporting. Collaborate with cross-functional teams to identify process improvements, create documentation, process maps, and user guides, and contribute recommendations to enhance operations and decision-making.

Lead advanced analytics and experimentation to improve digital customer experiences. Translate business questions into testable hypotheses, analyze structured and unstructured data (including transcripts and platform logs), build data models and visualizations, deliver insights to senior leaders, and drive measurement strategy and dashboard automation using SQL, Python, Snowflake, Tableau, Adobe Analytics and related tools in an Agile environment.

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.

The role leads the transformation of reporting and analytics for consumer product quality, focusing on complaint data analysis and driving quality improvements globally.

The role involves leading catastrophe modeling across insurance operations, executing strategies for underwriting and pricing, integrating risk analytics, and fostering innovation with advanced technologies.

As a Senior Data Analyst for Fraud Strategy & Operations, you will analyze data to identify fraud trends, build predictive models, design fraud prevention rules, and collaborate with cross-functional teams to enhance fraud detection capabilities.

As the Senior Fraud Strategy and Analytics Manager, you will analyze fraud trends, build predictive fraud models, implement prevention rules, and enhance risk profiles while collaborating with cross-functional teams in the financial services sector.

Lead investigations into KPI shifts, mentor Brand Analysts, bridge business and data science teams, and create innovative segmentation strategies.

The Manager will develop analytics and AI tools to answer business strategy questions and support decision-making within the Product Development and Supply team at Bristol Myers Squibb.

Build, maintain, and optimize data infrastructure and ETL pipelines; integrate data sources; ensure efficient, secure data collection and processing; implement quality control, documentation, and monitoring; support analytics and data science needs.

Design, build, and maintain scalable data infrastructure and ETL/ELT pipelines in cloud environments. Collaborate with data scientists and engineers to enable ML model development and feed software products. Translate stakeholder requirements, manage prototype-to-production transitions, and recommend new data technologies. Provide ad hoc engineering, POC development, and data analysis support.

As a Machine Learning Engineer, you will enhance internal libraries for deep learning, collaborate with researchers, and optimize workflows for cost savings.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Support DoD clients by performing workforce planning, data/decision analysis, BI reporting, dashboard development, predictive modeling, data validation, and communicating analytic findings to stakeholders using tools like Access, Excel, Power BI, and Tableau.

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.

The Jr. Data Engineer will design and maintain scalable data pipelines, develop AI/ML models, analyze complex datasets, and communicate insights across teams.

Develop predictive models using machine learning and 3D data, prepare data using cloud resources, and iterate solutions in a team-oriented environment.

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 a Compliance Data Scientist Co-op, you will automate data processing, enhance Power BI reports, and utilize AI for compliance improvements.

The Manager will establish advanced demand forecasting and supply chain analytics capabilities, collaborate with teams, develop models, and drive data-driven decisions for business strategy.

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