Jobs at Tiger Analytics

Develop and refactor Python optimization algorithms for supply chain, inventory allocation, replenishment, and pricing optimization. Build predictive models, analyze large datasets, design experiments, monitor KPIs, and provide data-driven recommendations. Collaborate with business stakeholders and data science teams to deliver analytical solutions, while coaching colleagues and applying advanced statistical and operations research methods.

Leads technology advisory, client engagement, presales, solution architecture, and practice development for enterprise data and AI transformations. Provides thought leadership, assesses client platforms, develops modernization roadmaps, creates architectures and proposals, and mentors engineering, data, and architecture teams. Requires deep hands-on Databricks and Azure expertise, experience with enterprise Lakehouse platforms, streaming and batch pipelines, governance, security, and large-scale transformation programs. Partners with senior executives and offshore teams to drive delivery quality, talent development, and consulting growth.

Designs and deploys advanced AI and machine learning solutions, including AI agents, RAG systems, analytical models, and data products on Google Cloud Platform. Responsibilities include preparing structured and unstructured data, developing AI agent tools, applying NLP and deep learning, collaborating with customers and stakeholders, generating business insights, and communicating analytical results.

Deploys, integrates, and scales enterprise generative AI solutions within customer engineering teams. Builds intelligent agents and multi-agent systems using Vertex AI, ADK, and MCP; connects AI systems to enterprise databases; develops real-time data pipelines; and operationalizes LLMs across cloud environments. The role also includes model training, tuning, evaluation, deployment, vector-based grounding, cloud infrastructure integration, stakeholder communication, and translating business goals into technical architectures.

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.

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.

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 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.

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.

Lead hands-on architecture, development, deployment, and optimization of enterprise GenAI, RAG, Agentic AI, and Neo4j Knowledge Graph solutions. Build data pipelines, APIs, microservices, and cloud-native AI infrastructure; rapidly deliver customer POCs and production systems. Serve as the primary technical interface for enterprise clients, troubleshooting performance, scalability, reliability, and cost issues while providing technical consulting.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

The Databricks Architect will design and implement large-scale data solutions, optimizing performance and reliability, while leading initiatives in data engineering.

The Machine Learning Engineer will develop software solutions, optimize data pipelines, and deploy machine learning models using cloud platforms and best practices.

The Technology Partner will lead technology solutions and data transformation initiatives for Fortune 100 clients, providing thought leadership and managing executive relationships.

Lead design and delivery of Snowflake-centric cloud data platforms. Engage with clients to gather requirements, architect scalable secure solutions, provide technical leadership, develop roadmaps, advise executives, and guide implementation best practices across the analytics stack.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

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.