Corporate Development

Corporate Development

Umbrex is pleased to welcome Matthew James with Materia Consultancy Ltd.  Matt was an Engagement Manager the McKinsey London office from ’98 to ’02 before joining the Mining & Metal industry in Australia, growing Lynas Corporation from a start-up to a $3 billion market cap ASX 100 company. In 2014 Matt returned to the UK and started Materia Consultancy Limited and worked as a freelance consultant, with a focus on industrial growth strategy projects, programme management and due diligence studies.

In 2017 Matt joined the global Executive Leadership Team of Harsco Corporation to implement the Environmental Services division growth strategy designed by Materia Consultancy. As VP Strategy, Business Development and Innovation, Matt was responsible for divisional strategy, acquisitions, strategic investments, and development of an innovation team.

Matt lives in Guildford, just outside London and has two children just starting high school.

Umbrex is pleased to welcome Nilesh Rajadhyax with Vyom Health.  Nilesh is a seasoned executive with 15+ years of experience in the Healthcare sector primary serving payers and providers. He was an Associate Partner at McKinsey and Company and advised clients on strategy, technology and acquisitions in healthcare. He served some large Health systems, a large national payer, a large healthcare technology company and many middle market private equity funds.

He then joined TransUnion’s Healthcare division and successfully, built a data business line from scratch. While at TransUnion he served as the GM for couple product lines, led the billing and sales operations functions and also led the merger and acquisitions team for the Healthcare division. He was part of TransUnion’s journey to IPO from a private equity ownership and understands the aspects of leading and operating under both ownership structures.

He most recently was with Heartland Dental, a large roll-up of ~1000 dental offices and was the Service line GM for Invisalign and Endodontics (root canals) across the organization. He led the marketing, training, payer management, financing and vendor management functions to grow the service lines and understands the mechanics of a multi-site provider network.

Nilesh would like to collaborate on projects of growth, process improvements, corporate development and strategy in the area of Healthcare providers, payers and Healthcare technology and services.

Umbrex is pleased to welcome Odd Utgård with The Real Consultancy.  Odd  spent two years with McKinsey in Oslo following his graduation from the London School of Economics. After McKinsey, he spent several years working with high tech startups and spin-offs from research institutes and universities, before founding the leading Norwegian tech incubator and seed fund – StartupLab. Odd’s key expertise is in growth and strategy, with a keen eye for business improvement opportunities and analytics.

He lives in Oslo, Norway and works primarily in the Nordics (but is happy to work globally once the pandemic allow him to!) Always interested in discussing business problems or opportunities and football over a coffee.

 

This article from Karthik Rajagopalan’s company blog introduces a type of machine learning that may provide solutions for some traditional optimization problems such as inventory optimization and supply chain optimization.

The field of artificial intelligence is slowly beginning to permeate our lives. Computers and other machines are being endowed with intelligence through a process called machine learning. One particular type of machine learning that is of interest to us here in Paramis Digital is Reinforcement Learning (RL), which has been quite successful in a limited number of applications like teaching computers how to play video games and teaching robots how to perform certain activities.

Supervised learning uses data with input and labeled output and learns the relationship between them. Unsupervised learning, on the other hand, works on unlabeled data and is generally used for pattern detection. It is not uncommon to find applications that use a combination of the two methods. In reinforcement learning an agent, say a robot, learns to perform activities in an environment to accomplish a specified goal. The agent learns by performing various activities, collecting feedback from the environment in response to the activities and evaluating the feedback. The key part of this learning method is the reward mechanism, a mathematical construct, which rewards the agent for performing activities that will move it towards and eventually accomplish the goal. The agent is also punished for performing activities that detract it from accomplishing its goals.

 

Read the full article, Reinforcement Learning, on the Paramis Digital website.