Martin Ignatov
EXPERTISE
BIO
I am a researcher at heart, drawn to new ideas. I enjoy diving deep into unfamiliar topics, taking the time to understand them thoroughly, and exploring different perspectives. I am most engaged when learning and building something meaningful as part of a team. I am looking for a fast-paced, analytically challenging environment with a steep learning curve that offers diverse responsibilities and the freedom to think creatively and build purposeful, impactful solutions.
WORK EXPERIENCE
Software Developer Internship
Dynamo Software
July 2019 - September 2025
Completed a full-stack internship at Dynamo Software, developing a web application in C# (.NET) integrated with SQL. Built a user-facing website to suggest corrections for misspelt words, using a custom algorithm for efficient Levenshtein distance calculation. Implemented dictionary search functionality with a prefix tree (Trie).
Quantitative Researcher
Trading 212
February 2023 - June 2023
Quantitative Researcher at Trading 212, specialising in analysing financial and market data. Developed machine learning models for time-series forecasting and enhanced data visualisation for event-driven systems. Improved system monitoring and performance by leveraging advanced statistical and computational techniques, significantly streamlining issue detection and reducing analysis time by half. Improved portfolio risk assessment and optimisation of financial resource distribution.
Researcher
Imperial College London
March 2022 - June 2022
Collaborated on research organised by the Imperial College Data Science Society on satellite image inpainting for LANDSAT 7 using models from the Neural Process family, specifically convolutional neural processes. My primary responsibilities were researching and evaluating NASA’s current impainting methods and evaluating and researching the models from the Neural Process family. I identified and implemented mathematical simulations of sensor malfunctions, contributing significantly during the early stages of algorithm development.
Researcher
Imperial College London
August 2020 - August 2021
Completed two summers of research at Imperial College London's Chemical Engineering Department, successfully demonstrating that reinforcement learning can be integrated with simulations of chemical engines to optimize chemical production. Researched and implemented policy-based RL algorithms using PyTorch, mathematically modelled complex biochemical constraints, and optimised simulated metabolic networks, showcasing RL’s applicability and potential in biochemical process optimisation.
EDUCATION
MSc
Imperial College London, September 2022
Physics
Verified Machine Learning Engineer
0-2 years of experience
Preferred commitment: Part Time
Languages
Skills
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