Jimmy.

This role was focused on driving the vision, execution, and delivery of high-impact data science initiatives that directly shaped Target’s forecasting and measurement capabilities. This role demanded a blend of technical depth, strategic foresight, and cross-functional leadership, focusing on moving projects from initial concept to full-scale enterprise adoption.

Project Leadership: Successfully led key strategic deliveries, including the No History Forecast (NHF) model, the DFE Measurement Framework, new store mapping, and demand-based store clustering algorithms.

Strategic Design: Designed the forecasting measurement framework for dual purpose—to support A/B comparisons and facilitate proactive system monitoring and root-cause analysis.

Communication: Drove adoption by effectively communicating business value and impact to non-technical partners, Directors, and Product Managers.

Accelerated Experimentation: Designed and built DFELite, a novel framework that slashed model evaluation runtimes from 10-20 hours to just 15 minutes for a full year of data, enabling rapid scientific iteration.

Impactful Delivery: Maintained a track record of shipping high-quality science and software, making major contributions to the core inference library and iterating on complex models like NHF and store clusters.

Mentorship & Team Development Mentorship: Provided direct mentorship and coaching to team members, including a Senior Data Scientist and an individual transitioning into analytics, fostering a culture of continuous learning and development.

This role focused on building and deploying advanced machine learning models to solve crucial forecasting and inventory challenges across Target’s massive Supply Chain and Merchandising operations. The team trained algorithms on tens of billions of data points to generate millions of demand forecasts.

Massive Efficiency Gains: I played a key role in rewriting a core forecasting capability in Scala. This work slashed model inference time from several hours to just 45 minutes.

Improved Accuracy: Through experimentation and testing, I implemented new models that delivered a measurable reduction in error across multiple departments, directly improving positioning and planning operations.

Built High-Performance Solutions: I developed and implemented a highly efficient item similarity algorithm. Leveraging techniques like salting, lazy evaluation, and mathematical optimizations, I reduced the workflow runtime from over 24 hours to 15 minutes.

ML Development Infrastructure: I created internal tooling, including a model comparison library and a mock data tool, to streamline the ML development lifecycle and ensure robust testing and deployment of forecasting systems.

Strategic Collaboration: I worked closely with Target’s global AI teams and business partners, analyzing complex enterprise data to identify new opportunities, develop effective AI solutions, and drive the large-scale implementation of models that supported critical enterprise decisions.

In this role, I was responsible for providing cross-company data science expertise and strategic technical leadership for various divisions, including Healthcare, Industrial, Food/Beverage, and Water. This demanded broad proficiency across multiple data science domains and required driving innovation from concept to deployment.

Launched Enterprise Forecasting: Served as the technical lead responsible for developing and deploying Ecolab’s first forecasting system designed to optimize the supply chain for bulk chemistry, delivering a critical capability for operations.

Broad Technical Versatility: Developed and deployed a wide range of models for real-world applications, including time-series forecasting, signal classification, product recommendations, chemical identification, and computer vision.

Innovation & IP Generation: Directly contributed to two major innovation projects that resulted in awarded patents, showcasing a commitment to advancing the company’s technological capabilities.

• Healthcare: Developed a human activity classification model for cleaning services, leading to a unique product launched in a major hospital system.

• Chemical Analysis: Created a product that combined low-resolution chemical scans with machine learning to provide cleaning solution recommendations.

MLOps & Deployment: Provided advisory and implementation support across diverse MLOps frameworks (Azure ML, Kubeflow, and vendor solutions) and technologies (cloud, on-prem). Created endpoints via Flask web apps, mobile apps (Android/iOS), and interactive dashboards to surface model predictions.

Leadership & Mentorship: Led and mentored a group of four interns through a successful innovation accelerator project, applying design thinking to solve novel business problems. Played an advisory role in next-generation hardware architecture working groups.

In this role, I was responsible for delivering end-to-end data science solutions across multiple divisions, including Healthcare, Industrial, Food/Beverage, and Water. This required a strong foundation in various data science techniques and the ability to translate complex business problems into actionable models.

• Develop models for a wide range of applications in the domains of time-series forecasting, signal classification, product recommendations, computer vision, and inventory management.
• Prototype software solutions to implement models - Flask web services, Android applications, and interactive dashboards.
• Work with a variety of data sources in a multiple ways - APIs, SQL, flat files, cloud based storage (Azure platform)
• Build automation pipelines (Azure/Kubeflow) to do various data science tasks including model training/prediction and model conversions
• Explored the use of Azure AutoML and CognitiveServices for quick project development
• Provide data consultation to multiple divisions. This includes exploring the possibilities for new projects as well as providing feedback to hardware engineers about data science needs and trends
• Constantly learning new technology including Azure resources, Docker , Kubeflow, Tensorflow, Android studio, Xamarin, object detection, time-series techniques, advanced signal processing, imbalanced classification, and more.