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Analyst- Site Merchandising Resume



  • 5+ years of experience in business analysis, consulting, data management and client relationship management with cross domain experiences across technical, retail, finance and e - commerce fields
  • Responsible for improving business KPIs through statistical modelling, demand forecasting, risk management and model validation/control
  • Effective liaison between business and technical groups, creative and resourceful, problem solving skills with excellent decision-making capabilities
  • Hands on analytics experience in solving problems in Website and Sales Analytics through the application of A/B testing and advanced statistical techniques such as segmentation, time series & demand forecasting and logistic regression
  • Experienced in predictive modeling, and statistical algorithm development using historical data and testing thresholds to predict future performance, and probability of conversions.
  • Experienced in development and design of reports and dashboards for supporting the business analysis in a corporate wide reporting using OBIEE, Business Objects, Tableau, Sharepoint and Visio
  • Skilled in SAS Programming, Macros, Preparing Data, PROCs, Producing Reports, Validating Reports, SAS Formats, Storing and Managing Data in SAS
  • Excel expert with experience in organizing data, maintaining dictionaries and utilizing VLOOKUP, power pivot, pivot reporting, functions, charts, and dynamic modeling for data cleansing and validation.
  • Extensive experience in Software Development full cycle (SDLC), Testing, Project Management, Client Engagement, Business logic development, BI/DWH/EDW data management/governance and managed multiple/concurrent engagements
  • Strong experience in the ETL (Extract, Transform and Load) of data into a DWH/DM and writing complex SQL/PL-SQL queries, OLTP, OLAP, Star, Snow Flake and multi-dimensional data modeling


Business Intelligence: Tableau, Omniture, OBIEE, Business Objects, Sharepoint, Essbase- Dashboards

Scripting Languages: SQL(Teradata, Hive, Dremel), PL/SQL, Javascript, Plx,VBA

Statistical tools: SAS, R Certification

Database: ETL Datawarehouse - (IBM Datastage,ODI), Oracle 10g/11g, Data-mining, Oracle EBS, EDI

MS Office: Word, Excel, PowerPoint, Access, Visio and Outlook.


Analyst- Site Merchandising

Confidential, California


  • Identified $3M in potential savings and increased profitability by monitoring margin trends and profit exceptions, evaluating pricing and cost of goods sold, freight costs, and utilizing revenue analysis by item, product and customer groups, to maximize the operational factors that affected business strategy
  • Created an omni-channel business strategy by evaluating digital programs, member and product experiences across all platforms to and drive growth by targeting specific customer segments, improve revenue, reduce operational costs and improve product offerings and inventory assortment
  • Maximized profitability, shipping and assortment planning by analyzing and comparing competitor strategies
  • Analyzed business efficiency and operational performances over the years and recommended ways to improve profitability and revenue through custom marketing strategies.
  • Developed dashboards and reports to analyze the performance of website, provide strategic analysis, and influence business leaders & client teams to implement marketing and product strategies based upon data-driven insights
  • Performed A/B testing on product releases to measure performance of KPIs and guide product development
  • Tracked performance of campaigns and analyzed impact on revenues, profitability and customer experience
  • Managed projects honing analytical techniques and reporting/statistical analysis skills

Business Data Analyst

Confidential, California


  • Worked with retail operations group to support operations for new product launches. Worked closely with retail partners/vendors to support data management and developed business intelligence solutions for reporting.
  • Led the setup for new product launches and new retail partners/vendors on-boarding in the data management and reporting infrastructure while ensuring the standard data security protocols.
  • Developed dashboards and reports to analyze and forecast sales, inventory to retail partners/vendors
  • Developed Excel automation solutions to decode, convert and analyze daily reports from various vendors

IT Analyst- Summer Intern

Confidential, San Francisco


  • Evaluated annual budget spend of $150M by working with vendors, analyzed key cost drivers, Identified cost savings of $1M annually and set benchmarks to realize the savings through contract negotiations
  • Streamlined the financial reporting process in the IT-Sourcing department to better accommodate dual reporting and eliminated operational redundancies in the process, automated reporting to analyze data from various data sources and created monthly ad-hoc reports
  • Identified Chargebacks among various business groups, identified gaps in processes and and enabled a repository of analytical tools and models
  • Developed Excel based tracking models using VBA to perform data cleansing, analyze the financial spend categories and automated the Excel models to generate compliance monthly/quarterly reports
  • Developed spend dashboards in Tableau and Oracle Essbase, to enable better short & long range planning, and supplier contract management with interactive views, trends and drill downs along with user level security to perform year over year, quarter over quarter, YTD, QTD and MTD type of analysis
  • Enabled mature financial reporting analysis & planning through various analytic models, which analyzes the budgeting & actual spend for services and labor provided by the vendors


IT Data Consultant


  • Led the restructuring of order management workspace to operate cross-functionally, as well as aid the users in dynamic reporting, eliminating the annual licensing fee of $500K
  • Retained the top suppliers in six regions by analyzing the trends and saved over $2 million a year
  • Incorporated comparative and regressive automation solutions, defined benchmark standards and eliminated about 20 man-hours/week
  • Developed predictive models which implemented customer conversion probability algorithms to identify patterns using historical, current data and improve the probabilities of customer conversions and successfully collaborated with planning, and Inventory management teams for efficient planning and Inventory management
  • Improved collaboration between teams by implementing new business process datawarehouse models and successfully integrated various business data models into one cohesive model
  • Designed and implemented data warehouse architecture for the business datawarehouse by creating data marts using conformed facts, dimensions, views, procedures, triggers, and functions using finely tuned SQL/PL-SQL queries to provide definition, structure and to maintain data efficiently
  • Enhanced operations and optimized business performance by building business models, simulating operations, developed business intelligence systems and strategic plans
  • Utilized Excel analytics to run regression and statistical significant tests for the business variables and built predictive models based on the results


Data Analyst- IT


  • Led the process optimization in the Customer Services division, by identifying the bottlenecks and trends in customer grievances division and attained a 20% increase in resolution rate for the first day
  • Implemented qualitative & quantitative solutions to solve complex business issues and effectively collaborated with management to initiate business process optimization
  • Developed complex PL/SQL queries to load databases through data validation and transformation
  • Tested, Cleaned, and Standardized Data to meet the business standards using Execute SQL task, Conditional Split, Data Conversion, and Derived column in different environments
  • Led the migration of business users to new data environment and helped migrate analytical reports
  • Led the effort on multiple data remediation challenges rising due to large data migration and data mismatches between OLTP and new warehouse

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