- Forward thinking data scientist, machine learning / software architect, and project manager with fifteen years of relevant experience. Excel in the design and implementation of machine learning solutions to a variety of problems both in academia and industry. Manage teams and projects building enterprise level production applications using a range of technologies and languages for clients spanning many industries. Conduct and present original scientific research in the fields of machine learning, data science, and computational biology.
Tools:, Technologies: Microsoft Azure, Azure ML Studio, Azure Machine Learning Services, Microsoft Cognitive Services, AWS, AWS Machine Learning and AI Services, Apache Spark, Hadoop, Hive, MapReduce, scikit - learn, Jupyter Notebooks, Encog, Apache Math, Excel.
Methodologies: Machine Learning, Probability and Statistics, Data Mining, Data Ingestion and Processing, Feature Engineering, Agile.
Storage Technologies: Oracle, SQL Server, Mongo DB, Data Lake, Data Warehouse, Cloud storage, DynamoDB.
Project Management Tools: JIRA, Confluence, TFS, VSTS, Trello, Azure DevOps.
Revision Control: Git, SVN, CVS, TFS, VSTS.
Confidential, New York, NY
Principal IT Consultant
- Food Products: Collaborated with a corporation to develop use cases; consulted on best practices for building and operationalizing software systems and data platforms. Scope included sales forecasting, customer segmentation, product affinity analysis, and recommender systems; achieved confidence levels of 90%.
- Manufacturing: Helped build a proof of concept application that leveraged machine learning to optimize inventory nationwide. Led team to build a full-scale data ingestion and processing platform on AWS to support additional uses cases and applications.
- Logistics: Used machine learning to help a moving company predict demand in specific geographical locations over time.
- Real Estate: Consulted on a project with a real-estate client in the appraisal of property and clustering of similar properties to aid agents using artificial intelligence.
- Point of Purchase: Led a team for a luxury vehicle client in building kiosk applications for Confidential show room. The applications spanned several disparate setups including full-wall screens and smaller configurations with which customers could interact.
- Energy Provider: Head data scientist for team at a retail energy provider that reduced load forecasting processing time from 8 hours to 5 minutes which allows savings based on more accurate buy/sell strategies on the retail market. Built out an analytics and data ingestion platform across the Microsoft Azure suite that leverages machine learning techniques to tackle business problems including customer acquisition, engagement, retention, and operational efficiency. It provided functionality for load forecasting, usage analytics, customer segmentation, predictive maintenance, competitor price forecasting, and customer lifetime value analysis.
- Retail Marketing: Developed machine learning systems for worldwide marketer of consumer and commercial products for a variety of use cases on Microsoft Azure stack. Projects included software for predicting units sold across several stores, customer segmentation, market basket analysis, and error detection in financial reports. Employed techniques for classification, regression, feature engineering, and more. Success of projects launched a full-scale big data and data science platform for future.
- Consumer Credit Reporting Agency: Collaborated on implementation of several Java web applications. Applications included the Order Funnel, Call Center, and Business Center; development included both client and server-side implementations. Projects involved design of the architecture as well as timeline planning, user story creation, and resource delegation. Acted as a team lead for the developers throughout the duration of the projects. Technologies included Java, Spring, Spring MVC, Spring Batch, AngularJS, REST, Hibernate, Maven and Oracle among others.
- Healthcare: Built a flexible model to predict a patient’s day-to-day risk of contracting a CLABSI infection; created algorithms for determining root causes for past outbreaks. Integrating CLABSI risk prediction into downstream decision support tools, alerts physicians to the risk of infection, enabling them to proactively take action to minimize the likelihood of the patient contracting CLABSI.
Confidential, New York, NY
- Collaborated on a wide variety of projects for the discovery phase of litigation. Added features to and improved the existing functionality and performance of a document review application.
- The application leveraged machine learning techniques and provided search, sort, analytics, categorization, classification, and clustering capabilities that aided in organization of relevant documents; projects involved both server side and client-side development.
- Technologies, methodologies, and languages used included C++, C#, PL/SQL, MFC, WCF, MSMQ, Oracle, SQL Server, and machine learning among others.