Solving Retail industry problems ranging from Supply Chain optimizations to what if Analysis for optimal pricing models, to optimize the spending on retail store by identifying the outlier stores using latest Machine learning and Data Engineering methodologies. Managing Client’s expectation and highlight risks. Responsible for end-to-end delivery from Global Delivery Center. Mentoring junior team members. Head in-house R&D team and work on latest trends in cutting edge technologies.

Supply Chain Optimization:Retail client needed to optimize their massive supply chain, involving ports, sorting facilities, distribution warehouses and retail stores. Helped them to gain insightful KPIs of incoming and outgoing pellets, trucks in their warehouses and retail stores across different forecasting horizon using demand forecasting ML techniques. I lead a team of Data Engineers, Data Scientist and Machine learning engineers to create a demand forecasting solution achieve this. This helped stake holders to gain better visibility into in-efficiencies and take corrective actions​Tech Stack: Python, Google Big Query, Airflow, R-Shiny dashboards.Smart Business Pricing Model : Retail client needed a solution for smart business growth by predicting the best pricing model by applying what-if analysis to their offerings. This was instrumental to compete with the hi-tech competitors. I lead a team of Data engineers, Data Scientists and ML engineers to create a demand forecasting framework and productionized it. This greatly helped the retailer in crafting an optimal pricing model to remain competitive in the market.​Tech Stack: Python, Pyspark, Google Big Query, Airflow, Google Cloud Storage.

Leading Data Governance, Data Profiling, Data Quality and Data cleansing in Pharmaceutical setting. Interacting with business stake holders, highlight potential risks and discuss resolutions. Guiding on data security and data compliance issues.

Led database administration and data architecting efforts and successfully met Service Level Agreements (SLAs) for 99.9% uptime for mission critical applications for MLB.com and associated partners. Led the team in developing data pipeline for ingestion and persistence of MLB’s streaming data using AWS ecosystem.Environment: AWS Kinesis, S3, Couchbase (NoSQL DB), Teradata, Teradata Viewpoint, EC2, puppet, Jenkins, python, Oracle, PL/SQL, SQL.

Designed, coded and implemented MLB’s business intelligence solution which provided clarity and insight into the e-commerce and streaming data and helped executives to make better business decisions and improved customer satisfaction.Created and architected backend NoSQL database for MLB’s media player telematics data with single digit millisecond latency achieving multimillion operations/second throughput.Environment: Sun Solaris, Oracle, Couchbase, AWS, EC2, PL/SQL, Erwin, Jaspersoft.

Successfully developed backed procedures to support MLB’s e-commerce application, which greatly reduced development time and improved efficiency.Developed zero-tolerance credit card selection logic framework to bring multi-million dollar revenue for the company.Architected MLB’s feed syndication system which gave business users flexibility of managing data feeds with better monitoring and alerting which greatly reduce unknown failures and delivery delays.Environment: Oracle, Sun Solaris. PL/SQL, SQL. Erwin, Toad.

Built data pipeline to feed the MVC financial system which efficiently ingest, standardize and transform the data for target system.Successfully conducted interview sessions with SMEs to understand legacy driving license agency system to build data model for MVC next generation system.Environment: Sun Solaris, AIX, Oracle, Datastage

Successfully changed the backend database engine from cache to Oracle for Styleclick’s 3D Visual Merchant application with not only 100% functionality restoration in addition achieved improved overall application performance and stability.Environment: Windows Server, Microsoft foundation Classes. C++, Oracle