Top Tech Jobs & Startup Jobs in NYC, NY

Design, develop, and operationalize time-series and ML-based forecasting models for demand planning, inventory, replenishment, logistics, and sales forecasting. Collaborate with stakeholders, lead workshops, translate business needs into scalable analytics, and present insights to senior client teams.

The Google Workspace Administrator will manage and integrate Google Workspace environments, support M&A efforts, and collaborate on security and identity management processes, while automating tasks through scripting.

Lead end-to-end delivery of a Plant Performance Reporting transformation across manufacturing plants, manage executive stakeholders and governance, coordinate cross-functional and offshore teams, prioritize Snowflake data engineering and Power BI productionalization, oversee UAT and deployment, and drive adoption, training, and reporting standardization.

The Databricks Architect will design and implement data architecture solutions using Databricks, AWS, and Snowflake, focusing on MLOps and data governance.

Design and lead the development of cloud-native Generative AI platforms and solutions, ensuring scalability, security, and integration with enterprise applications.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

The Gen AI Data Engineer will design and build distributed data systems, develop data pipelines, manage data infrastructure, and integrate technologies for real-time and batch processing, contributing to scalable analytics solutions.

Lead portfolio-level Data & AI strategy and technical governance for enterprise clients. Advise C-suite, drive architecture and modern data stacks, win high-value engagements, and scale engineering and data science teams.

Lead design and governance of enterprise ontologies, knowledge graph architecture, ingestion pipelines, and semantic layers. Enable semantic interoperability across data products, implement inference and reasoning, and establish standards, versioning, and data contracts to support scalable graph-driven analytics and AI augmentation.

Lead business development and growth in AI/ML, provide technical leadership in data science solutions, and enhance client engagement by delivering strategic data insights.