Jobs at Tiger Analytics

Lead the design of data architectures in Multi-Cloud environments, oversee migration of legacy systems, develop predictive models, and mentor technical teams.

The Alteryx Lead Consultant will analyze business requirements, design data flow processes, and support the migration of Alteryx workflows to tools like Snowflake and Power BI while focusing on cost reduction and performance optimization.

Lead BI consulting engagements: gather stakeholder requirements, design data architecture and ETL/data warehouse solutions, build and validate dashboards (QuickSight), manage offshore delivery, ensure data quality, and drive risk mitigation and process improvements.

Design, develop, and deploy time-series and machine-learning forecasting models (ARIMA, SARIMA, ETS, XGBoost, LightGBM) for demand planning, inventory, and sales forecasting. Perform large-scale SQL data processing, operationalize models in cloud environments (Azure preferred), present insights, and lead client workshops.

Lead architecture, design, and delivery of enterprise-scale Agentic AI solutions. Oversee multiple engineering pods, mentor teams, manage client-facing architecture discussions, ensure production-grade MLOps, cloud infrastructure, microservices, observability, and adoption of emerging AI technologies.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

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.

The Finance Director will lead the finance team, overseeing financial planning, analysis, and management practices to support the company's growth and performance optimization.

Lead modernization and design of applications, architecting cloud-native systems, optimizing APIs, and ensuring security and scalability for enterprise applications.

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.

This role leads Revenue Growth Management initiatives, linking data analytics with executive strategy to drive business growth through actionable insights and project management.

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.

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.