Poonam Saini Email and Phone Number

Retail Advanced Analytics: An analytical suite providing powerful set of applications designed totransform data into actionable insights across critical business areas. ∗ Crafted automated store-level reporting with intuitive dashboards and visualisations, offering a comprehensive view of key metrics and KPIs.∗ Leveraged cutting-edge machine learning algorithms to identify and mitigate fraudulent activities inside stores.∗ Enabled historical data analysis to help uncover patterns and trends in fraudulent behaviour r enabling proactive risk management.Scaled Consumer Engagement: A suite of Machine Learning and Power BI applications to under- stand the interactions with Consumers and Members. These meticulously crafted apps include Consumer Segmentation , Consumer & Membership Analysis and Campaign Analysis, each tailored to address dis- tinct facets of consumer engagement and insights.∗ Leverage sophisticated machine learning algorithms to segment consumer base into distinct groups based on purchasing behaviour, demographics, and other key factors∗ Streamlined reporting process with automated, scheduled reports that deliver consistent and up-to- date information directly to stakeholders∗ Leverages advanced statistical methods and machine learning algorithms to automatically detect outliers, whether they represent errors, fraud, or rare events that warrant further investigationSupply Optimisation: Ensuring sufficient stock is always available to meet expected demand. Buying & Planning need a trust-able recommendation tool, that provides a reliable, understandable forecast by bringing underlying information, and known limitations.∗ Engineered & Optimised Rolling Demand Plan solution on AWS cloud platform (BDP), saving costs by 25%.∗ Designed a global solution for EU market, easily scalable to other markets, reducing the Time to mar- ket by 40%.∗ Architected platform agnostics solution foreseeing Databricks as new tech stack to be adopted company wide.

Responsible for integrating and maintaining new datasets to central warehouse, creating daily/weekly/- monthly aggregates, delivery of reports and dashboards.◦ Architected Reconciliation Engine solution on which n sources can be reconciled by providing required configurations, reducing reconciliation efforts by 70%.◦ Designed On demand Merchant Reporting solution, where merchants are served with in a defined SLA using this framework◦ Migrated ETL framework of Python and Hive to PySpark (including daily runs, error handling, and log- ging) to clean useful data and create facts an OLAPs.

Designed multi-dimensional modelling (Star and Snowflake schemas) for data warehouse applications.◦ Performed Source System Data analysis as per the Business Requirement. Distributed data residing in heterogeneous data sources is consolidated onto target Enterprise Data Warehouse database.◦ Worked closely with the business users to understand the requirements and converting them into project level technical capabilities.

Analyzed the sources, targets, transforming and mapping the data and loading the data into targets.∗ Involved in extraction of data from the different flat files and data from Oracle OLTP Systems.∗ Developed ETL transformations, mappings, sessions and workflows.∗ Created Slowly Changing Dimension (SCD) Type 2 mappings for developing the dimensions to main-tain the complete historical data.

Created, optimized, reviewed, and executed Teradata SQL test queries to validate transformation rules used in source to target mappings/source views, and to verify data in target tables.∗ Performed tuning and optimization of complex SQL queries using Teradata Explain.∗ Performance Tuning of sources, Targets, mappings and SQL queries in transformations designing, creating and tuning physical database objects (tables, views, indexes, PPI, UPI, NUPI, and USI) tosupport normalized and dimensional models.∗ Created proper Primary Index taking into consideration of both planned access of data and even dis-tribution of data across all the available AMPS.∗ Configured the mappings to implement Slowly Changing Dimensions (Type-2).