Kostas Nikolakopoulos, Quant Developer in London, United Kingdom
Kostas Nikolakopoulos

Quant Developer in London, United Kingdom

Member since December 18, 2020
Kostas is a data scientist and quantitative analytics specialist focusing on developing predictive models using machine learning techniques. He worked with multiple clients in the financial services sector in projects such as future balance predictions, credit risk modeling, and simulation engines. Kostas has extensive coding experience in Python, R, and C++ and academic background in theoretical physics with a doctoral degree from Sussex University and an MSc degree from Imperial College.
Kostas is now available for hire




London, United Kingdom



Preferred Environment

TensorFlow, Jupyter Notebook, RStudio, Visual Studio, PyCharm

The most amazing...

...performance increase I've implemented brought down the time needed to run a model from days to minutes.


  • Software and Data Engineer

    2022 - 2022
    Reddit, Inc.
    • Developed algorithms to optimize advertisement revenue.
    • Implemented model changes in Scala to include new features.
    • Researched increasing revenue through better budget pacing techniques.
    Technologies: Data Science, Distributed Systems, Software Engineering, Go, Scala, Python, Java, Spark, BigQuery, ETL, Mathematics, Quantitative Analysis, Numerical Analysis, Algorithms, Back-end Development, Machine Learning, Google BigQuery, Machine Learning Operations (MLOps)
  • Python Quantitative Researcher

    2021 - 2021
    Tickup (Algo Fund)
    • Designed and developed an algorithmic trading platform that connected multiple development components and technologies.
    • Onboarded trading strategies from quant workspaces into the trading platform; performed backtesting and parameter optimization under different scenarios.
    • Cleaned, managed, and consolidated data prepared for the go-live version.
    Technologies: Python, Go, SQL, Machine Learning, Data Engineering, Data Science, Quantitative Analysis, Quantitative Modeling, Quantitative Research, Pandas, Python 3, Quantitative Finance, Finance, Machine Learning Operations (MLOps)
  • Credit Risk Quant

    2018 - 2020
    Bank of America Merrill Lynch
    • Delivered an IRC/CRM regulatory project dictated by Brexit migration requirements.
    • Enhanced aspects of the model to better reflect theoretical requirements and historical behavior. Conducted statistical tests and submitted them to the validation department.
    • Improved the performance of the model implementation. Identified the current model's properties, which reduced the execution from days to hours.
    Technologies: SQL, Python, C++, Data Science, Data Engineering, Quantitative Analysis, Quantitative Modeling, Pandas, Python 3, Quantitative Finance, Finance
  • Flow Rates Quant

    2018 - 2018
    BNP Paribas
    • Contributed to the pricing and risk platform of an electronic transformation project.
    • Implemented pricers and risk across rates products such as swaps, bonds, and futures.
    • Enhanced the C++ library for pricing and risk calculations.
    Technologies: Python, C++, Quantitative Analysis, Quantitative Research, Quantitative Modeling, Finance, Quantitative Finance
  • Quant Developer

    2017 - 2018
    Bank of America Merrill Lynch
    • Collaborated with the model performance team to backtest the bank models for all asset classes.
    • Improved and enhanced the Python codebase and user interface.
    • Used Python and C++ coding for the simulation of risk factors and correlations, applied for calculating profit and loss, XVA, and margins.
    Technologies: C++, Python, Simulations, Data Engineering, Pandas, Python 3, Finance, Quantitative Finance, Quantitative Analysis
  • Behavioral Modeler

    2016 - 2016
    Royal Bank of Scotland
    • Led the behavioral modeling team in preparation for separating the Williams & Glyn division of the Royal Bank of Scotland.
    • Developed predictive behavioral models for residential mortgages and current or savings accounts. The models' owner was the treasury, using them for the purposes of funds transfer pricing (FTP) and interest rate risk management.
    • Coordinated the development of the Python library for the team and developed a web-based GUI for business users to run the models.
    Technologies: Python, Machine Learning, Scikit-learn, Data Science, Data Engineering, Quantitative Analysis, Quantitative Modeling, Quantitative Research, Finance, Quantitative Finance
  • Python Quant Modeler

    2014 - 2016
    Barclays Bank, PLC
    • Developed predictive behavioral models for various portfolios of the bank's investment, corporate, and retail parts using historical time series data; was personally responsible for the residential mortgage book and the corporate term loans book.
    • Managed the full lifecycle of the models, from data cleaning to presentation and documentation of results. Performed ad-hoc statistical analyses, scenario analyses, backtesting, and model reviews.
    • Contributed to the quant analytics grad training, gaining exposure to all the departments of the bank.
    • Performed ad-hoc statistical modeling and statistical data analysis for various projects of the team.
    Technologies: SQL, C++, Python, Machine Learning, Scikit-learn, Artificial Intelligence (AI), Quantitative Analysis, Quantitative Modeling, Quantitative Research, Finance, Quantitative Finance
  • Consultant

    2013 - 2014
    d-fine, Ltd.
    • Conducted current accounts modeling for a major bank based in Vienna.
    • Developed a supervisory mechanism for the EU bank regulator.
    • Gained exposure and experience in the large-scale application architecture.
    Technologies: SQL, Java, C++, R, Python


  • House Price Prediction

    I developed a Jupyter notebook-based application for the UK house price predictions and trends. There is an optional data scrapping module to refresh most up to date prices.

    The client can input their property characteristics such as postcode, number of bedrooms, or garden, and get the estimated price. There is also an add-on feature for trend predictions based on property characteristics.

  • Sports Arbitrage App

    A Python application to detect real-time arbitrage opportunities in the sports market and place appropriate bets. The application constantly reads the odds from a big list of betting websites and identifies the optimal positioning of bets. There is a possibility to place bets for the websites that allow API connections.


  • Languages

    Python, C++, R, SQL, Java, Go, Python 3, Scala
  • Libraries/APIs

    TensorFlow, Scikit-learn, Pandas
  • Tools

    PyCharm, Visual Studio, BigQuery
  • Paradigms

    Data Science, Quantitative Research, ETL
  • Platforms

    RStudio, Jupyter Notebook, Amazon Web Services (AWS)
  • Other

    Mathematics, Mathematical Modeling, Statistical Modeling, Statistical Data Analysis, Data Analytics, Statistics, Data Modeling, Machine Learning, Quantitative Analysis, pyton 3, Artificial Intelligence (AI), Simulations, Web Scraping, Bayesian Inference & Modeling, APIs, AWS, Data Engineering, Quantitative Modeling, Quantitative Finance, Finance, Distributed Systems, Software Engineering, Numerical Analysis, Algorithms, Back-end Development, Google BigQuery, Machine Learning Operations (MLOps)
  • Frameworks



  • Doctoral Degree in Theoretical Physics
    2009 - 2013
    University of Sussex - Sussex, UK
  • Master's Degree in Theoretical Physics
    2007 - 2008
    Imperial College London - London, UK
  • Bachelor's Degree in Physics
    2001 - 2006
    Aristotle University of Thessaloniki - Thessaloniki, Greece

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