Create, automate, and streamline end-to-end AI/ML processes to enhance operational efficiency. Manage a team consisting of a data engineer, data scientist, DevOps engineer, infrastructure engineer, along with external solution partners from Microsoft and Databricks, resulting in the successful delivery of corporate production AI platform and projects. Ensure the timely completion of projects by establishing a robust framework for AI/ML deployment, integrating project management, data/feature/ML engineering, model creation, and CI/CD model deployment. Utilize YAML pipelines to support deployment patterns, ensuring automated/streamlined continuous integration and continuous deployment processes for the models by deploying CI/CD in Azure DevOps. Employ successful model deployments using Azure machine learning to Kubernetes, Databricks to Azure Machine Learning to Kubernetes, and end-to-end Databricks, resulting in flexible and scalable deployment options for the models within the organization.Additional achievements are:● Secured an estimated cost saving of $1.0M by employing the first production machine learning solution, attributing cost reduction to improved accuracy in product weights reducing fines, fees, and tariffs across the supply chain.● Transitioned proof of concept model from research to production environment by coaching and guiding Masters-level interns, internal tech partners, and vendor partners while simultaneously designing ML platform and enhancing end-to-end processes.● Designed, developed and implemented a single inference "meta" model consisting of over 200 submodels with dynamic categorical encoding unique to each submodel enabling accurate predictions based on unique product characteristics. The “meta” model handles real-time requests through a REST API and batch jobs through dataframe input, ensuring seamless integration and flexibility in both online/offline prediction scenarios.

Oversaw the integration of incentive program data with a SaaS vendor, involving extraction from a SQL Server data warehouse, transfer to Azure Data Lake Storage, matching to vendor's specifications using Spark tables in Databricks, and securing the transmission to the vendor via SFTP. Assured a smooth transition and alignment between the old/new systems by delivering expert consultation and support during the implementation of the new financial reporting system through leveraging in-depth knowledge and experience as a subject matter expert on the legacy system and processes.Additional achievements are:● Revamped and re-architected the systems and processes underpinning the largest company incentive program, accommodating $1.7 billion worth of qualified transactions, enhanced scalability and reliability.● Translated legacy financial system design and processes as a critical subject matter expert during the implementation of a new financial reporting system in OneStream resulting in a smooth transition to the new system while ensuring accurate processing and presentation of financial statements.● Minimized incentive program reporting cycle time from a monthly cadence to a daily reporting cycle by automating manual processes and systematically capturing siloed incentive logic, third-party customer hierarchies, and data quality rules.

Drove data-driven decision-making by C-suite and upper management in new product development by redesigning the product reporting system by deploying a product data management system. One of my key tasks was to automate and optimize the process of the weekly sales promotions reporting cycle from 3-4 days to a remarkable 30 minutes, enabling quicker analysis and steering sales strategies.Additional achievements are:● Streamlined and enhanced the monthly reporting process for the sales team handling over $2B in sales, reducing the processing time from 5 days to just 3 hours. This improvement was achieved by utilizing enterprise tools and comprehensive data sets, ensuring faster reporting and increasing process robustness and accuracy.

Developed daily dashboards using Hyperion Web Analysis by coordinating with internal sales, product and finance teams. Tracked daily orders, backlog, shipments, and inventory by utilizing customized dashboards. Executed all testing activities according to schedule, resulting in the timely deployment of the upgraded system into production, with absolutely no disruptions to system usage. Drove changes to Hyperion systems/reporting to incorporate significant organizational changes including product and channel value streams and SG&A cost centers.Additional achievements are:● Spearheaded and orchestrated migration testing and validation for the seamless transformation from Hyperion version 9 to Hyperion version 11, ensuring a flawless upgrade.● Enabled accurate and comprehensive management of products across the organization by integrating over 100 K items from a legacy system into Oracle and seamlessly connecting downstream transactional and reporting systems. ● Analyzed and selected second-phase business intelligence reporting tool as part of selection team. Evaluation process included requirements gathering, vendor interviews, and proofs of concept from vendor finalists.● Coordinated development of freight expense and freight forecasting process to systematize the reporting and accrual of outbound freight expense. The freight estimation component of this process increased the accuracy of the monthly freight accrual by 71%.● Coordinated development of sales cube based on invoiced transaction data. The cube also integrated light source reporting to support third-party license agreements and light source reporting to both internal decision makers and external industry groups.● Developed and coordinated migration testing and validation for upgrade from Hyperion version 9 to Hyperion version 11. All testing was competed as scheduled and upgrade went into production on time with no impact on usage.