Jobs at Tiger Analytics

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.

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.

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

The Data Product Manager will lead the development of enterprise data products by translating business needs into scalable data solutions, managing analytics projects, and coordinating with global teams to ensure efficient delivery and optimization of data initiatives.

Lead pricing operations and technology strategy to design scalable, cloud-native, AI-driven pricing solutions. Oversee architecture, data science model development, Agentic AI adoption, SDLC, integrations, and stakeholder alignment to operationalize pricing strategy and drive margin optimization.

Lead delivery of AI and analytics solutions for a financial services client: manage end-to-end projects, run discovery workshops, translate requirements into product roadmaps, assess Agentic AI vs rules-based approaches, align Databricks Lakehouse and Unity Catalog architectures, and coordinate stakeholders and hybrid delivery teams.

Support stakeholders by profiling, mapping, and validating financial reconciliation datasets. Build complex SQL queries and Python workflows, and use Snowflake and Databricks to design, transform, and manage large-scale finance data for reporting, reconciliation, and analytics. Ensure data quality, perform root-cause analysis, and support governance and documentation.

Lead analytics consulting engagements: translate business problems into end-to-end data science and ML solutions, present to executive stakeholders, manage projects and global teams, and operationalize analytic outputs with IT and data engineering.

The Manager, BI Consulting will lead BI initiatives, engage stakeholders, analyze requirements, design data solutions, and manage client relationships.

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

Lead end-to-end analytics consulting engagements: translate business problems into analytics roadmaps, drive program delivery, quantify ROI, align executive stakeholders, establish governance and performance dashboards, and operationalize ML and data solutions across functions.

Lead development and scaling of enterprise data products for supply chain: translate business needs into end-to-end analytics solutions, manage delivery, coordinate cross-functional teams, build data roadmaps, ensure data accessibility, and present results to senior stakeholders.

Lead complex data and analytics programs, manage stakeholders and global teams, design AI/ML solutions, and validate outputs in the Insurance domain.

The Director of Analytics Consulting will lead analytics projects for CPG clients, translating business problems into high-level solutions and managing global teams, with a strong emphasis on data-driven insights and stakeholder engagement.

The role involves managing analytics projects, engaging with clients to translate business problems into analytics solutions, and presenting results to senior management while collaborating with global teams.

Consultant/Sr Consultant responsible for applying data science solutions to business challenges, managing analytics projects, and engaging with stakeholders.

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.

Manage end-to-end US and Canada recruiting for GenAI, ML, and Data Engineering roles: source, screen, maintain ATS and pipelines, coordinate interviews, manage offers/negotiations, and partner with external agencies.

The Technology Partner leads client engagements in Data & Insights, providing technology leadership, thought leadership, and mentoring while driving transformation agendas and solutions development.