James Watson Email & Phone Number

London, Gb

London, England, United Kingdom

Equinox project, FactSet FundamentalsLanguages & technologies: Python, FastAPI, VueJS, AWS, Redis, SQL, Attic (FactSet database abstraction API), FactSet.io (container and deployment tool).• Optimised financial concept collection logic, reducing loading times from approx. 45s to 3s.• Increased stability for document and table loading, helping achieve a 99.99% application uptime.• Decoupled in-app validation and implemented Cerberus (internal validation tool).• Elimination of > 140k data inaccuracies between metadata cache and database.• Implemented over 75% backend API testing coverage.Financial Investment Group pipeline, FactSet Deep SectorLanguages & technologies: AWS, PostgreSQL, Python, C#, AngularJS, Protobuf, Attic, FactSet.io, FQL (FactSet Query Language, written in C++).• Took ownership of pipeline to restore production capabilities.• Implemented export process and integrated with client facing architecture, aligning with FactSet innovation initiatives for buy-side clients, contributing to annual service revenue of over $1bn.• Creation of new data model to adhere to internal standards, migrated client facing database from AWS Aurora PostgreSQL to Attic.• Stability champion – Implementing performance alerting for AWS and internal FactSet resources.

London, England, United Kingdom

Lead a fast-paced, agile team focussed primarily on developing tools to automate or assist with the testing of pricing changes.Technologies: Python, Snowflake & PostgreSQL, Jenkins.• Automated batch tests for new pricing rules to replace manual testing. This halved testing time and allowed for a complete set of risks to be tested each time, rather than having them tailored to the current changes.• API load performance testing following the implementation of new pricing models.• Oversaw a large project to ensure automatic insurance renewals were compliant with recent legal requirements. Developed a pipeline to collect and validate data, testing against current pricing models and generate a full report to deliver information to key stakeholders.• Automation of processes and tasks within financial modelling software to allow pricing team members to work more effectively and with higher confidence.• Real-time, interactive data dashboard to allow for assessment of price changes post deployment.• Manage and upskill team members, performing regular code reviews and tests.

Cambridge, United Kingdom

Introduced machine learning to the business, training models to classify significant driving events • New incident classification improved from approximate a 40% successful notification rate to 76%. Once deployed this information was reported to customers in real time. Develop processes and tools to monitor and analyse product performance, taking data from hundreds of thousands of IoT devices and convert these into information to enable customers to assess risk and identify upselling opportunities• Battery state detection algorithm allowed a client to have real time information across a fleet of electric vehicles.• Categorised self-install device removal events to detect unauthorised removal. This work was presented to CTO and drove physical hardware redesign to improve physical security.• Device activity, ensuring KPIs were being reached, and allowing for the early identification and resolution of any issues. Ad-hoc analysis and presentation • Creation of clear and concise incident analysis and reconstruction reports for insurers and authorities, often to be presented as legal evidence. • Supply chain analysis increasing profit margin by approximately 160% on a consistent revenue stream. Internal quality• Measure performance and identify any areas of risk or weakness, recommending and implementing improvements, assessing the effectiveness of changes made.