Kostas Nikolakopoulos, Developer in Athens, Central Athens Regional Unit, Greece
Kostas is available for hire
Hire Kostas

Kostas Nikolakopoulos

Verified Expert  in Engineering

Quant Developer

Location
Athens, Central Athens Regional Unit, Greece
Toptal 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 on 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.

Portfolio

Adam Aerospace Co., LLC
Python, Machine Learning, Natural Language Processing (NLP)...
Gynisus Inc.
Artificial Intelligence (AI), SQL, Large Language Models (LLMs), Databases, PDF...
Visual Candy Systems Limited
Scikit-learn, Python, Capital Markets, Machine Learning

Experience

Availability

Part-time

Preferred Environment

TensorFlow, Jupyter Notebook, RStudio, PyCharm, Visual Studio

The most amazing...

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

Work Experience

AI Engineer

2023 - PRESENT
Adam Aerospace Co., LLC
  • Developed an MVP using GPT and publicly available data to optimize its performance.
  • Experimented and investigated functionalities of LLMs and possible applications to different aspects of the product.
  • Maintained the end-to-end infrastructure and monitoring of the AI part of the application.
Technologies: Python, Machine Learning, Natural Language Processing (NLP), Minimum Viable Product (MVP), OpenAI, Data Lakes, Dashboards, Data Visualization, Flask, Finance, Finance APIs, ChatGPT, Full-stack, Large Language Models (LLMs)

LLM Expert via Toptal

2024 - 2024
Gynisus Inc.
  • Worked on parsing large documents in order to extract reporting rules.
  • Tried different LLMs to judge their suitability for the problem.
  • Tried different modeling approaches to assess their suitability.
Technologies: Artificial Intelligence (AI), SQL, Large Language Models (LLMs), Databases, PDF, Data Extraction

Capital Market Expert

2023 - 2024
Visual Candy Systems Limited
  • Provided consulting on optimizing algo-trading FX modeling for the client.
  • Investigated current performance drivers and optimized feature selection.
  • Performed back-testing of historical trades to determine the impact on profit and loss.
Technologies: Scikit-learn, Python, Capital Markets, Machine Learning

AI Developer

2023 - 2023
Samer Abualsoud
  • Developed a trading algorithm based on FX time series using ML/AI techniques.
  • Performed back-testing on historical trading data.
  • Developed the infrastructure to upload the model online for live trading.
Technologies: Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning, Python, Trading

Data Science Consultant

2023 - 2023
Acuity Trading LTD
  • Consulted with the internal team on alternative data applications in financial modeling.
  • Prepared training presentations covering various aspects of alternative data usage, including price prediction and portfolio construction.
  • Analyzed internal data to create case studies on possible applications in financial modeling.
Technologies: Data Science, Systematic Trading, Quantitative Research, Algorithmic Trading, Large Language Models (LLMs)

Lead Quant Developer

2022 - 2022
SPS Trading
  • Developed the dev library for algorithmic crypto trading.
  • Designed and developed trading API for quants to interact with the trading systems.
  • Implemented and onboarded algorithmic trading models into production.
Technologies: Python 3, Python, API Design, API Development, C++, C++14, AWS CLI, AWS DevOps

Machine Learning Engineer

2022 - 2022
PepsiCo Global - Main
  • Developed the Bayesian model for ad allocation using a very long list of features from ad campaigns.
  • Trained and maintained the Bayesian model for ad allocation on AWS.
  • Performed analysis and presentation of the model's outcomes, specifically feature importance, p-values, and more.
Technologies: Python, Machine Learning, SQL, Amazon Web Services (AWS), Docker, Bayesian Inference & Modeling, Bayesian Statistics, Jupyter Notebook, Visual Studio, Statistical Data Analysis, TensorFlow, Artificial Intelligence (AI), Data Modeling, Data Scientist, Data Science, Data Analysis, Dashboards

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), Jupyter Notebook, Statistical Data Analysis, Data Scientist, Data Analysis, Dashboards

Data Scientist

2021 - 2021
Grata Inc
  • Created an MVP for estimating a company's value from public data.
  • Participated in data gathering and building data pipelines.
  • Delivered a stand-alone Python app to run a machine-learning model for live valuations.
Technologies: Data Science, Python, Economics, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), PyTorch, Machine Learning, Statistics, Perl, R, Data Pipelines, Jupyter Notebook, Statistical Modeling, Statistical Data Analysis, Data Analytics, Data Modeling, Data Scientist, OCR, Data Scraping

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), Deep Learning, R, Data Pipelines, Financial Forecasting, Artificial Intelligence (AI), Jupyter Notebook, Visual Studio, Statistical Modeling, Statistical Data Analysis, Software Engineering, TensorFlow, Data Analytics, Data Modeling, Data Scientist, BigQuery, Data Analysis

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, C#, Data Pipelines, Financial Forecasting, Jupyter Notebook, Visual Studio, Statistical Modeling, Statistical Data Analysis, Artificial Intelligence (AI), Data Analytics, Data Modeling

Flow Rates Quant

2018 - 2018
BNP Paribas
  • Contributed to the pricing and risk platform of an electronic transformation project.
  • Implemented prices 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, C#, Data Pipelines, Financial Forecasting, Jupyter Notebook, Visual Studio, Statistical Modeling, Statistical Data Analysis, Data Analytics, Data Modeling

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 to simulate 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, C#, Visual Studio, Statistical Modeling, Statistical Data Analysis, Data Analytics, Data Modeling

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, Statistical Modeling, Statistical Data Analysis, Data Analytics, Data Modeling

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 bank departments.
  • 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, Financial Forecasting, Statistical Modeling, Statistical Data Analysis, Data Modeling

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.
2009 - 2013

Doctoral Degree in Theoretical Physics

University of Sussex - Sussex, UK

2007 - 2008

Master's Degree in Theoretical Physics

Imperial College London - London, UK

2001 - 2006

Bachelor's Degree in Physics

Aristotle University of Thessaloniki - Thessaloniki, Greece

Libraries/APIs

TensorFlow, Scikit-learn, Pandas, PyTorch, API Development

Tools

PyCharm, Visual Studio, BigQuery, AWS CLI, ChatGPT

Languages

Python, C++, R, SQL, Java, Go, Python 3, Scala, C#, Perl, C++14

Paradigms

Data Science, Quantitative Research, ETL, Testing

Platforms

RStudio, Jupyter Notebook, Amazon Web Services (AWS), Docker, Google Cloud Platform (GCP)

Storage

Data Pipelines, Data Lakes, Databases

Frameworks

Spark, Flask

Other

Mathematics, Mathematical Modeling, Statistical Modeling, Statistical Data Analysis, Data Analytics, Statistics, Data Modeling, Machine Learning, Artificial Intelligence (AI), Quantitative Analysis, Simulations, Predictive Modeling, Web Scraping, Bayesian Inference & Modeling, APIs, Data Engineering, Quantitative Modeling, Quantitative Finance, Finance, Distributed Systems, Software Engineering, Numerical Analysis, Algorithms, Back-end Development, Google BigQuery, Machine Learning Operations (MLOps), Deep Learning, Bayesian Statistics, Economics, Natural Language Processing (NLP), Financial Forecasting, Generative Pre-trained Transformers (GPT), Data Scientist, OCR, Writing & Editing, Systematic Trading, Algorithmic Trading, API Design, AWS DevOps, Data Scraping, Fine-tuning, Models, Data Analysis, Dashboards, Capital Markets, Large Language Models (LLMs), Minimum Viable Product (MVP), OpenAI, Data Visualization, Finance APIs, Full-stack, Trading, PDF, Data Extraction

Collaboration That Works

How to Work with Toptal

Toptal matches you directly with global industry experts from our network in hours—not weeks or months.

1

Share your needs

Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2

Choose your talent

Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3

Start your risk-free talent trial

Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.

Top talent is in high demand.

Start hiring