Data Science

Leadership on your terms

Transform your work with the power of data; AI/ML strategy, data engineering, business analytics, predictive modeling, model-as-a-service, onsite education.

  • Discover, explore and scope the right problems to solve.
  • Build the models and apply the algorithms to predict the next-best-action to produce the best outcome.
  • Optimize data ingestion and transformation and data science infrastructure.
  • Assign responsibility for continued enhancement and operational risk mitigation to the right resource.
  • Educate your engineering team and encourage them to stay at the forefront of your endeavor.


  • AI/ML Strategy
  • Predictive Modeling
  • Data Engineering
  • Business Analytics
  • Model-as-a-Service
  • Onsite Instruction

Data Science for Healthcare on the Google Cloud Platform

Data Science may seem like an overnight sensation. But, the practical tools within Data Science like Natural Language Processing and Computer Vision and Predictive Modeling are decades in the making.

Domain expertise and experience is essential for success when applying Data Science to healthcare informatics.

The Google Cloud Platform is the most advanced Data Science infrastructure available today.

So, work with a firm who brings domain expertise, technical experience and a strong GCP partnership to bear on your solution.

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Data Science on the Google Google Cloud Platform enabled this healthcare startup to match patients with the most appropriate clinical trials by using Natural Language Processing to interpret thousands of clinical trial protocols and electronic medical records and Predictive Modeling to predict highest enrollment and greatest completion rate.

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Onsite Instruction

Grow your in-house Data Science capacity and retain enthusiastic technoligists by giving them the time, space and onsite training to advance their knowledge at the forefront of Data Science.

Data Science for Managers

A practical introduction to data science for business and technology managers challenged to deliver business value with investments in data science.

This 4-hour instruction module provides an overview for business executives, line managers, and recruiters charged with building Data Science capabilities. Topics include:

  • What is Data Science?
  • Overview of machine learning
  • Marketplace of data science tools & techniques
  • Data architecture, data warehouses and business intelligence
  • Project scoping and budgeting
  • Appropriate project processes

Practical Machine Learning

An intensive, hands-on introduction to machine learning, open source tools and statistical programming languages. Once a niche set of tools for programmers and quants, ML has become indispensable for a wide array of applications.

This 30-hour course teached the practical aspects of machine learning relevant to students in the current role and business context. Upon completion of the course students will be able to apply ML to problems in their own work.

Assignments will reinforce lectures and are tailored to students’ business and data context.

Data Engineering & Data Infrastructure

Capture events and extract data from a plethora of sources and push data through a transformation pipeline to an Analytics model to support human and machine decision-making. All on a highly efficient infrastructure that expands and contracts with immediate resource needs.

Agile staffing for Data Engineering helped this SaaS company build a team of 20 engineers on a contract-to-hire basis, to develop configurable infrastructure-as-code features to add to an acquired Analytics-as-a-Service product supporting Bring-Your-Own-Data functionality for their customers.

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Data Engineering for Business Analytics helped this successful SaaS company build an enterprise business intelligence platform for accurate and timely operational analytics guiding decision-making from tactical sales to board level strategy and investor presentations. Including, Annual Recurring Revenue, Customer Lifetime Value and Customer Acquisition Cost. Time-series and multi-version data models enabled incremental additions of data sources and historical reconciliation for a truly Agile architecture.

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Business Analytics

Investigate and explore performance and gain insight to shape new marketing, sales and operations actions.


Model-as-a-Service enables organizations to accelerate their analytics evolution by centralizing Data Engineering expertise to make secure models populated with reliable data available to Data Scientists and business analysts across the enterprise.

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