Jobs at Tiger Analytics

Support enterprise data governance by documenting end-to-end data lineage, designing ADS components, building GCP data pipelines and data quality controls, contributing to data modeling and target-state design, and acting as a bridge between engineering, enterprise architecture, and business stakeholders while presenting findings to senior leadership.

As a Solutions Architect, the role involves leading solution architecture in PLM and supply chain systems, driving integration efficiency, analyzing business processes, and ensuring technical adherence for production releases.

The Strategy Consultant will lead bi-weekly reviews on allocation performance, conduct root-cause analyses, identify issues, and provide strategic recommendations for retail clients in the fashion industry.

Lead delivery pods for enterprise data governance and ADS architecture: design target-state ADS, document data lineage, implement aggregation-layer controls, conduct SME interviews, translate policy into delivery plans, manage analysts, select governance tooling, and report KPIs to senior stakeholders.

Embed with client engineering teams to operationalize LLMs and retrieval systems across multi-cloud environments. Build agentic solutions using Vertex AI Agent Builder and ADK, connect agents securely to BigQuery/Spanner via MCP, implement streaming pipelines (Dataflow) for real-time inference, and manage model training, tuning, deployment, and evaluation on Vertex AI. Provide consultative guidance, cross-functional collaboration, and clear stakeholder communication to ensure business value from Generative AI solutions.

The Senior Data Scientist will leverage advanced analytics, machine learning, and AI to develop analytics engines and collaborate with teams to enhance business value and communication of results.

The Integration Engineer will configure Adobe Workfront, develop integrations using Workfront Fusion, and optimize marketing workflows and automation processes. They will collaborate with multiple teams and manage project tracking within the Workfront platform.

Lead requirements gathering and documentation for system upgrades, coordinate testing efforts, and act as a liaison between stakeholders and technical teams.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions on-site, collaborating with engineering teams to operationalize AI systems across multi-cloud platforms.

Lead design, development, and deployment of end-to-end recommendation systems. Build, tune, and evaluate scalable recommendation algorithms, integrate ML into products, lead projects, mentor teams, and communicate solutions to clients and stakeholders while driving innovation in replenishment and supply chain analytics.

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.