Top Data & Analytics Jobs in Buffalo, NY

The Senior Research Scientist will architect and build pipelines for training models on driving data, develop and optimize prediction models, and mentor junior engineers while collaborating with perception and motion planning teams.

The Data Engineer will design, develop, and maintain SQL data warehouses, optimize stored procedures, build reports using SSRS, create dynamic dashboards with Superset, and manage data pipelines via Airflow for data integration and automation.

The Supply Chain Senior Analyst will oversee supply chain operations for various e-commerce micro-brands, managing inventory levels, procurement, vendor relationships, and fulfillment. Responsibilities include implementing inventory best practices, collaborating with logistics providers, developing supply chain strategies, and ensuring compliance with Amazon guidelines.

The Principal Data Scientist will lead the predictive analytics practice, focusing on deploying machine learning models for financial metrics and customer forecasts, collaborating across teams to enhance operational capabilities and model performance.

As a Data Architect, you will design, implement, and maintain a robust data architecture, supporting advanced analytics and business intelligence initiatives. You will collaborate with cross-functional teams to integrate data from various sources, create and maintain data models, establish data governance policies, and identify performance optimization opportunities.

The Data Architect will design and optimize the organization's data infrastructure to align with business goals and enhance data-driven decision-making. Responsibilities include developing data solutions using various Azure services, managing data architecture, ensuring compliance with regulations, and collaborating with cross-functional teams to meet data infrastructure needs.

As a Senior Data Scientist in Fraud Detection, you will analyze and mitigate fraud risks using advanced analytics and machine learning. Collaborate with product and engineering teams to build data-driven solutions and improve fraud detection strategies while maintaining an understanding of current trends.