Jobs at Tiger Analytics

Develop and optimize assortment strategies using statistical forecasting and data analysis. Analyze product, inventory, and financial metrics, partner with stakeholders, produce data-driven recommendations, monitor performance, and support demand planning initiatives.

Partner with senior leaders to drive workforce planning, talent and performance management, PIP governance, HR analytics, employee relations, retention and succession planning, policy and compliance, and culture and change initiatives supporting North America-based employees.

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.

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.

Design and implement high-performance APIs and microservices using Python and AWS, focusing on generative AI and scalable applications.

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.

As a Senior Agentic AI Engineer, you will lead innovation in AI initiatives, from ideation to prototyping, collaborating with teams to deliver impactful solutions.

Lead analytics consulting delivery, manage client relationships, drive account growth, and collaborate with teams to develop strategic analytics solutions for CPG and Retail clients.

Lead the requirements gathering, analysis, and documentation for banking systems. Collaborate with stakeholders to define functional requirements and drive end-to-end project delivery, ensuring compliance with banking standards.

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 deploy causal inference and predictive models (uplift, synthetic control, DML) for customer loyalty, churn, and LTV. Build foundational data models and scalable features, perform large-scale SQL ETL, operationalize models in cloud, partner with stakeholders, and present insights to drive revenue and loyalty program optimization.

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 enterprise-scale cloud and data modernization initiatives, assessing and planning migrations across data, applications, and cloud platforms for clients, ensuring alignment with business goals.

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.

Design and maintain cloud-native applications and microservices primarily in Python on AWS, while collaborating with teams for backend and frontend integration.

As a Machine Learning Engineer, you'll develop scalable ML solutions using Google Cloud Platform and Vertex AI, collaborate with teams, and operationalize models from ingestion to monitoring.

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

The Senior DevOps Engineer will develop and maintain automation solutions, support network design, ensure high availability, improve monitoring, and handle incident management.

The role involves leading analytics delivery to clients, managing relationships, ensuring account growth, and solving complex business problems through data science in various sectors, primarily CPG and Retail.

Lead high-impact data science initiatives, build ML models, manage analytics projects, communicate insights, and collaborate with teams to drive business outcomes.