General gadabout, specific scapegrace, mostly decorative.

Leading a dynamic team in delivering an innovative data platform to enterprises. Overseeing the development and implementation of cutting-edge solutions that streamline data management and analysis, driving execution across the entire business line including sales and operations, and engaging directly with current and prospective clients

Building the platform powering insights delivery to industries via turn-key solutions built on top of the data cloud.Forging the future of data usage by providing the context, metadata, and ontological connections that make it possible to not just *find* data, but to make it more useful by *truly understanding what's in it* and *how it connects to the wider world of data*. Delivering category-creating features on an industry-leading SaaS platform by building teams: - Machine Learning and Data Science (US and Russia) - Data Engineering (US and India) - Data Operations (India) - Data Project Managers (US)Working arm-in-arm with Product and Engineering to plan and execute on creating a software platform that will finally help realize the promise of freely moving, totally discoverable, easily acquirable data for everyone.

Driving effectiveness and efficiency for the federal government through the application of data. Built and led a team of machine learning engineers and data scientists to execute on delivering clients contextualized insights from data spanning industries. Enabled user workflow optimization with models embedded in the product that supported and sped up process to discover connections among data and mission needs. Helped define and execute on company strategy for acquiring new clients. Created roadmap for new capabilities and implemented execution plan across teams.

Created new deep-learning NLP models for conducting large-scale entity recognition in datasets that crossed topics and industries. Fielded production code to SaaS platform. Developed pipelines for model training and continuous learning from new data. Sped up time to release on features by optimizing methods for working across engineering, data engineering, and machine learning teams.

Identifying the best applications of data science to industry problems.Crafting what to teach the machine and guiding how it learns to help solve those problems. Coaching initiatives from idea to delivery. Ensuring the quality and fit of the final product to support business growth.

We help build data science products that help new companies grow faster, expand their reach, and break new ground. Whether it's starting from the foundations with good data hygiene for capture and storage or fielding models to help constantly improve business processes, we are putting data science to work where it does the most good. Let the problem drive the solution: every option, from basic descriptive statistics to the cutting edge in deep learning architectures is on the table. Personally, I work across the spectrum: early discovery through exploratory data analysis to building a system that automatically selects among competing machine learning algorithms to improve results for a growing business.

Applying statistical methods to questions in health care utilization and outcomes. Predictive modeling for innovating new medical procedures, tracking patient behavior, and planning business decisions for fielding new procedures and shaping insurance coverage. Bouncing around from Python to R to SAS, tossing in a little SQL, Hive, Spark, and even a little Tableau. I work through the entire pipeline from identifying and collecting useful data, storing/retrieving on a range of SQL/NoSQL databases, data cleaning, imputation, feature engineering, dimension reduction, modeling -- running from the usual scikit-learn spectrum of supervised and unsupervised methods to deep learning using TensorFlow and PyTorch backends through Keras -- and delivery in static and dynamic presentation formats.

Develop and deploy production-level machine learning algorithms to predict movements in energy market prices. Translate market rules into model features to reflect deregulated nature of energy industry. Apply results of modeling to solve customer needs in scheduling energy consumption/production. Assess scope and size of market opportunities for kWantera products. Engage with customers to translate model results into effective decisions.Key Analytical Methods: -Classification and prediction models-Distributed computing for large scale data storage and use-Statistical metrics for model evaluation and performance of results

Design, budget, and execute research on large-scale national security programs. Manage research process and teams through initiation, conduct, client delivery, and final publication of findings. Communicate analysis and findings to senior policymaking leadership, including mixed expert/non-expert audiences.Key Analytical Methods: -Sophisticated economic & cost-modeling for billion-dollar programs-Regression-based statistical analysis-Simulation modeling

Designed and conducted quantitative analysis of policy options for clients, including NASA, U.S. Air Force, and the Office of the Secretary of Defense. Key Analytical Methods: -Agent-based modeling & simulation-Probabilistic queuing models & combinatorial optimization