Data Science Project Capabilities

Our experience is that data science toolsets and mathematics are high-value, general skills. They are necessary, but not sufficient for project success. The blockers for most data science project success are these key capabilities:

  1. Data domain expertise - the team must have long term exposure to the data sets
  2. Platform ETL expertise - the team must know how to access, transform and secure data from domain specific platforms, most of which are legacy and pre-cloud
  3. Application domain expertise - the team must have expertise in the project goals (business/industrial goals)

The Lever10 Data Domain Expertise:

The Lever10 team has executed major data science projects with leading companies such as Standard and Poors, VMware, HPE, A10 Networks, Zuora, Gigamon and many smaller VC funded startups.

Primary data science projects:

ROMI Analysis - Return on Marketing Investment

New Market Indicators - emerging evidence of new markets from marketing and CRM data

Inventory inefficiencies (many projects), cross transfer inefficiency, hidden stock, wrong distance shipping, bundle sizing, and many more options

Target customer optimization from CRM data

Pricing optimization studies, in vitro and in vivo

SaaS event discovery and user behavior shaping

Saas in-app event capture and modeling

Custom RPA (Robotic Process Automation) - see

Billing system revenue leakage

Machine Learning projects for inventory, pricing (time-of-day, day of week, seasonal) optimizations

The above list is more representative than complete. We have been doing applied data science since 2005 using tools from every major vendor. Recently, we have focused on the DataRobot toolset and we can assist the AutoML deployments and especially connecting working ML to legacy platforms for feedback loops. We are also expert at the DataRobot Zepl notebook toolset and have performed many successful Zepl projects.

Contact Us for more details or to start a project.

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