If You Can Meet with Triumph and Disaster, and treat those two impostors just the same;

The Situation: Daily P&L production, risk generation, and product control for a USD Rates exotic derivatives business

The Problem: Late nights spent closing books. Fourteen-hour days, every day. Hours wasted due to inefficient daily processes. Staff turnover running at 100% every six months due to long hours. A business incapable of producing daily risk numbers. Loss of business trust and morale at rock bottom.

The Solution: I fundamentally rebuilt every aspect of the team. I forged an all-new group of high-performing individuals into the best team in the business. I built my own pricing and risk spreadsheets that were lighter, faster, and far more informative than anything that came befor them. I rebuilt entire risk dictionaries and pricing schemes from the ground up. And within three months, we had the trust of the business again.

The Situation: Acting as an in-house systems and processes expert advising the USD Rates daily product control teams.

The Problem: Every day some new issue pops up with P&L that cannot be properly attributed using the Federal Reserve’s framework for classification. The numbers are unacceptably and persistently high and there is no clear indication as to where the problem lies.

The Solution: I built rapid-pricing prototypes that precisely replicated our risk systems to calculate and attribute P&L for any given trade in any given business. I deployed these across US Rates, Credit, and Securitised Trading. I analysed trades with serious unexplained P&L noise and broke down the issues with clear explanations for the business. I solved issues by adding pricing dictionaries, reporting configurations, and raising issues to our quant teams for continuous improvements. The end result: a huge improvement in the relationship between the business and Finance.

The Situation: Product control for a USD Bermudan Options portfolio

The Problem: The business needs to know what impact specific changes in market pricing parameters will have upon the value and risk of its portfolio. Unfortunately, nobody knows how to use any of the existing tools to deliver this in anything under three hours.

The Solution: Working with the IT team, I deployed a new rapid-pricing tool that allows end-users complete control over pricing scenarios and inputs, batches the jobs to remote pricing grids, and returns impact and difference reports. After spending a few days familiarising myself with the platform, I started using this tool to give the business exactly the intelligence it needed. The result was a far clearer picture into the risk management of the books and a far more agile business that could set aside reserves for its most difficult-to-manage positions with ease.

The Situation: Oil prices in 2008 rose to record levels, sparking fears (again) of peak oil and global supply shortages. Airlines, in particular, are desperate to figure out how to hedge their exposures.

The Problem: With only one or two conspicuous exceptions, the airline industry as a whole has never attempted to hedge out kerosene prices using forward contracts before and needs expert advice on how to do so.

The Solution: By putting together a basic simulation model using NYMEX-traded options on crude oil futures contracts, I demonstrated how even a basic hedging program could radically decrease overall portfolio volatility and improve business profitability. The findings were assembled and presented at the Energy Risk Conference in Houston in 2008 as part of a broader Enterprise Risk Management (ERM) framework presentation. Since that time, active fuel-cost hedging programs at airlines have grown significantly in size, scope, and effectiveness around the world.

The Situation: A client looking to migrate existing data analytics and visualisation tools from its own legacy servers to Microsoft’s Azure Cloud Computing platform.

The Problem: Microsoft’s Azure platform is completely new technology for everyone – the company AND our client. Nobody knows how to use it, and nobody knows what it can really do.

The Solution: Starting with Microsoft’s free all-access trial, I began testing a number of scenarios for Extract, Transform, and Load (ETL) exercises. I built out a series of workflows in Azure Data Factory to read, write, and transform data into cloud-based SQL databases. I demonstrated how to combine Azure’s Logic Apps framework with calls to ADF to generate automated alerts when data processing was finished, or to throw errors if any were encountered. Simultaneously I wrote out a training manual showing how a complete beginner could become fluent with Azure’s platform within a few days of effort and learning. The results gave the client a very clear idea of the benefits, and the costs, of moving to the cloud, and allowed our client to develop its own approach to the migration process.