Data Science

Data Science Consulting

For over 25 years, Fulcrum Analytics has been a leading provider of data science, advanced analytics, and data engineering. Our experienced team of on-shore consultants work closely with client end-users and stakeholders to provide:

  • Strategy development
  • Data assessment
  • Data augmentation, integration, and maintenance
  • Predictive modeling and statistical inference
  • Supervised deep learning with neural networks
  • Generalized linear models
  • Machine learning modeling
  • Cluster analysis
  • Principal component analysis
  • Classification models
  • Data visualization
  • Time series modeling
  • Forecasting
  • Natural Language Processing (NLP)
  • HR analytics
  • Share of wallet analysis
  • Risk analytics
  • Loss driver analysis
  • Unstructured data mining
  • Big data programming
  • Data anomaly detection
  • Management dashboards
  • Recommendation systems
  • Website analytics

We work across industries

We can help by deploying our resources on a per-project basis or with a consulting team-based approach.


Fulcrum's Data Science Acceleration Team

Our Data Science Acceleration Team (DSAT) 2.5 is great for any business looking to ramp up their data capabilities. A DSAT 2.5 package comes equipped with a team of interchangeable specialists fitted to your needs. To optimize your engagement outcomes, a mix of data scientists, engineers, developers and the project lead will work the equivalent of two and a half full time resources. Whether you are a small startup with no data science team or a multi-billion dollar financial institution with more projects than the bandwidth you have to support, DSAT 2.5 can help move you forward.

CPU icon

Our staff is trained in a wide range of programming languages, methodologies and frameworks including:

  • Python: Pandas, NumPy, Scikit-learn, Matplotlib, PyTorch, Keras
  • R: tidyverse, Shiny  
  • SAS: Base, EG, STAT, GRAPH, IML  
  • SQL: Hive SQL, PL/SQL, MySQL, PostgreSQL, Impala
  • Data visualization: R Shiny, Superset, Dash-Plotly
  • Distributed computing: Cloudera Hadoop,Druid, Spark, Hive, Impala, HBase, Kafka
  • Container/Virtualization: Docker, Kubernetes, OpenStack
  • Development: Agile methodology, Git, Atlassian (Bitbucket, JIRA, etc.), Jupyter, R-Studio, Unit testing,  Modular, Object Oriented design principles  
  • Web development: Flask, Django, Javascript, Bootstrap, and D3.js, API development

Team Lead

Richard Vermillion

Richard Vermillion


Richard Vermillion has served as Fulcrum’s CEO since 2011, and was the company’s CTO from 1996 through 2011. In his former role, he was the chief architect of Fulcrum’s technology products and service offerings.

As the CEO, he led a management buy-out of Fulcrum in 2013 and built up Fulcrum’s modern data science and engineering capabilities. In 2015, he led the spin-out of After, Inc., the leading provider of warranty analytics solutions, and acted as After's CEO until its successful sale in 2017. Now full-time at Fulcrum, Richard continues to contribute to the development of the company’s technology platform and to key client relationships.

Before joining Fulcrum, Richard worked for McKinsey & Co. He holds a B.S.E. in Mechanical and Aerospace Engineering from Princeton University.

Recent Thoughts on Data Science

When it comes to evaluating the impact of business decisions to the bottom line, there are a few common steps Read more
One thing we’ve all learned in the last year is how to work without our colleagues being steps away. When Read more

How we have helped clients

A startup needed to scale up their data science and engineering capabilities in short order. They used DSAT 2.5 for our deep financial industry expertise and advanced data engineering skills. We provided them with the right mix of technical and domain knowledge to rapidly accelerate their client service.

A global investment bank uses DSAT 2.5 to modernize tagging, tracking and reporting for their research portal. Also, we are creating a test and learn framework to enable more efficient experimentation and comprehensive feedback.

A retailer wanted to understand the attractiveness of their local markets and how they fared against their competitors. This retailer used DSAT to automate the generation of custom detailed reports, comprised of an (automatic) aggregation and analysis of various public data sets, and develop an interactive application allowing business users to create further customizable charts and reports.

To learn more about ways we have aided our clients with data science please check out our case studies