Eugene is available for hire
Hire EugeneEugene Balkind
Verified Expert in Engineering
Data Scientist and ML Developer
London, United Kingdom
Toptal member since June 13, 2022
Bio
Eugene is a skilled data scientist with a strong academic and industrial background in time series analysis, LLMs, and other ML technologies. Eugene has created classification models that predict positive or negative outcomes of COVID-19 tests and models that determine whether a company is a good acquisition. He has also built data hubs, completed cross-validation testing, and adjusted and improved models to adapt to quickly changing requirements. He is also proficient in OpenAI API.
Portfolio
McKinsey & Company
Agentic AI, Agentic AI Systems, Large Language Models (LLMs)...
Wheelhouse Interactive, LLC
Python, TensorFlow, NumPy, Xarray, Bayesian Inference & Modeling, Bash...
Next
Python, SQL, PySpark, Databricks, MLflow, NumPy, SciPy, Pandas, TensorFlow...
Experience
- Python - 9 years
- Machine Learning - 9 years
- Time Series Forecasting - 8 years
- Artificial Intelligence (AI) - 5 years
- Marketing Mix Modeling - 5 years
- Large Language Models (LLMs) - 4 years
- Agentic AI - 3 years
- Databricks - 2 years
Preferred Environment
Python 3, TensorFlow, Pandas, Mathematics, Regression, Amazon Web Services (AWS), SQL, ChatGPT, Amadeus, Azure
The most amazing...
...project I've done was COVID-19 testing automation. Lab performance improved from 300 analyzed samples a day to 30,000, with the ability to go up to 100,000.
Work Experience
Senior Data Scientist
2025 - 2026
McKinsey & Company
- Owned end-to-end delivery and technical architecture for a firm-wide AI agent, deployed to internal users across enterprise workflows.
- Built LangChain-based agentic workflows combining RAG, multi-step reasoning, database querying, and tool orchestration.
- Designed retrieval patterns, database interaction, orchestration logic, evaluation flows, and reliability controls for complex enterprise use cases.
- Implemented observability, logging, guardrails, fallback behaviors, and Opik-based quality tracking to improve reliability and support continuous iteration.
- Built automated evaluation and testing workflows to compare model and agent variants, detect regressions, assess reasoning quality, and identify hallucination risks, edge cases, and failure modes.
- Partnered with engineering teams to deploy the system using Docker, GitHub CI/CD, APIs, and maintainable software engineering practices.
Technologies: Agentic AI, Agentic AI Systems, Large Language Models (LLMs), Large Language Model Operations (LLMOps), Light LLMs, Cursor AI, GitHub Copilot, Opik, RAG Systems, RAG Pipelines, RAG Architecture, Agentic RAG Systems, Retrieval-augmented Generation (RAG), AI Agents, AI Compliance Agents, Model Context Protocol (MCP), Vector Databases, Prompt Engineering, Benchmarking, Agentic Coding, Agentic Frameworks, Agentic Workflow Design, Artificial Intelligence (AI), AI Tools, AI Testing, Generative Artificial Intelligence (GenAI), Python, Python 3, FastAPI, OpenAI, OpenAI API, OpenAI GPT-4 API, Claude, Claude API, Anthropic, ChatGPT API, ChatGPT Prompts, SQL, Docker, YAML, YAML Pipelines, Agent Evaluation, API Integration
Marketing Mix Modeling Data Scientist
2025 - 2025
Wheelhouse Interactive, LLC
- Migrated an existing MMM solution from PyMC to Google Meridian, adapting model structure, workflow logic, and implementation patterns for production use.
- Partnered with DevOps to deploy the solution within the client’s infrastructure and engineering standards.
- Improved maintainability, reproducibility, and production readiness of the MMM codebase for ongoing marketing effectiveness analysis.
Technologies: Python, TensorFlow, NumPy, Xarray, Bayesian Inference & Modeling, Bash, Bash Script, SSH, AWS SSH Keys, Terminal, Git, A/B Testing, Marketing Mix, Marketing Mix Modeling, Marketing Science, CRM, ROI, Probabilistic Modeling, Marketing Analytics, PyMC, Google Meridian, Bayesian Statistics, Regression Modeling, Data Analytics (Marketing)
Senior Data Scientist
2025 - 2025
Next
- Designed and delivered a modular Bayesian Marketing Mix Modeling suite using PyMC and Google Meridian, enabling rapid iteration across brands, channels, and planning scenarios.
- Built production-ready MMM workflows in Databricks with MLflow experiment tracking and model registry, improving reproducibility, auditability, and model comparison.
- Owned end-to-end MMM delivery from data preparation and model diagnostics through to scenario planning, budget optimization, and stakeholder recommendations.
- Applied Causal Impact analysis where data volume or campaign structure was insufficient for robust MMM, enabling pragmatic measurement of campaign uplift and business impact.
- Partnered with marketing and finance stakeholders to embed MMM outputs into budget planning, ROI analysis, and post-campaign evaluation.
Technologies: Python, SQL, PySpark, Databricks, MLflow, NumPy, SciPy, Pandas, TensorFlow, Scikit-learn, PyMC, Google Meridian, Causal Inference, Marketing Mix, Marketing Mix Modeling, Marketing Attribution, Bayesian Inference & Modeling, Marketing Science, CRM, ROI, Incrementality Testing, Probabilistic Modeling, Marketing Analytics, Data Analytics (Marketing), Bayesian Statistics, Analysis of Variance (ANOVA), Regression Modeling, A/B Testing, Multivariate Statistical Modeling, Funnel Marketing, Hyperparameter Tuning, Model Evaluation, Model Validation, Machine Learning Operations (MLOps)
Lead Data Scientist
2023 - 2025
Tropicana Brands - Main
- Led the machine learning function, setting strategic direction across different business areas.
- Enhanced supply chain planning accuracy by 13% by building a demand forecasting system using Databricks on Azure, deep learning, LightGBM, and Prophet, leading to reduced operational costs and environmental impact.
- Launched NLP customer-complaints analytics using SQL, FastAPI, and HuggingFace to identify churn drivers, product-quality issues, and customer dissatisfaction from unstructured feedback.
- Delivered a planogram extraction tool using the OpenAI API to automate in-store layout checks and compliance review.
- Led development, evaluation, and deployment of an enterprise LLM assistant for Tropicana Brands Group using Llama, LangChain, RAG, pgvector, Chroma, and Elasticsearch.
- Designed prompts, retrieval patterns, source-reference behaviors, and evaluation criteria for correctness, reasoning quality, and robustness.
- Used Opik to compare LLM and retrieval variants, identify hallucination risks, weak retrieval behaviors, reasoning gaps, and failure modes, and then iterated on system design to improve reliability.
- Packaged the LLM assistant as a FastAPI service and deployed it on Azure using production-focused engineering practices.
- Set technical priorities and coordinated delivery across applied AI, commercial analytics, and stakeholder-facing data science workstreams.
Technologies: Data Science, Machine Learning, Python, SQL, Predictive Modeling, Forecasting, Azure, Time Series, Bayesian Machine Learning, Marketing Mix Modeling, Large Language Models (LLMs), Open-source LLMs, Large Language Model Operations (LLMOps), OpenAI, OpenAI API, Meta Llama, Llama API, Llama 3, OpenAI GPT-3 API, OpenAI GPT-4 API, LangChain, Databricks, Azure Databricks, Docker, NumPy, SciPy, Pandas, Scikit-learn, Natural Language Processing (NLP), TensorFlow, Keras, LightGBM, Flask, FastAPI, Spark, Spark ML, Linux, Git, Bash, Jira, Hugging Face, Time Series Data, Time Series Analysis, Time Series Forecasting, Predictive Analytics, Statistical Modeling, Scenario Analysis, Demand Forecasting, Data Engineering, Agentic AI, Agentic AI Systems, AI Agents, Model Context Protocol (MCP), AI Tools, AI Testing, Vector Databases, Prompt Engineering, Opik, Benchmarking, Agentic Frameworks, Retrieval-augmented Generation (RAG), Generative Artificial Intelligence (GenAI), API Integration, Optical Character Recognition (OCR), Bayesian Inference & Modeling, Marketing Science, Marketing Mix, Probabilistic Modeling, Bayesian Statistics, Regression Modeling, A/B Testing, Multivariate Statistical Modeling, Logistics & Supply Chain, Supply Chain, Supply Chain Optimization, SAP Supply Chain Management (SCM), Inventory Management, Inventory, Sales Forecasting, Gradient Boosting, Decision Trees, PyMC, lightgbm, XGBoost, JAX, Agentic RAG Systems, Hyperparameter Tuning, Model Evaluation, Model Validation, Machine Learning Operations (MLOps)
Marketing Mix Modeling Data Scientist
2024 - 2024
Minoro LTD
- Reviewed and validated TensorFlow-based Marketing Mix Models to confirm model integrity, improve confidence in performance outputs, and support data-driven marketing investment decisions.
- Designed a new suite of Marketing Mix Models using Bayesian statistical methods and PyMC to improve model interpretability, uncertainty quantification, and decision reliability.
- Modeled full-funnel sales performance to evaluate the impact of marketing activity across the customer journey, from awareness through to conversion.
- Applied adstock and saturation transformations to capture lagged media effects, diminishing returns, and the non-linear relationship between investment and sales response.
- Built repeatable modeling workflows and documentation to improve transparency, governance, and scalability of Marketing Mix Modeling capabilities.
Technologies: Machine Learning, Deep Neural Networks (DNNs), Marketing Mix Modeling, Data Science, Marketing Attribution, Inventory Management, A/B Testing, PyMC, Bayesian Inference & Modeling, Marketing Science, Marketing Mix, Probabilistic Modeling, TensorFlow, Python, SQL, DuckDB, NumPy, SciPy, Scikit-learn, Marketing Analytics, Snowflake, Bayesian Statistics, Analysis of Variance (ANOVA), Regression Modeling, Multivariate Statistical Modeling, Funnel Marketing, Data Analytics (Marketing), Hyperparameter Tuning, Model Evaluation, Model Validation, Machine Learning Operations (MLOps)
Senior AI/ML Predictive Modeling Engineer
2023 - 2023
What Are the Chances
- Developed an NLP algorithm using Transformers and PyTorch that identifies rude and bullying responses. This involved understanding the nuances of language and identifying harmful interactions.
- Created an algorithm based on the OpenAI API (GPT-3.5-Turbo, which powers ChatGPT) that predicts the approximate probability of any event.
- Designed an ecosystem to process and store data using SQL, pandas, and AWS. This allowed for streamlined data management.
- Deployed the model on AWS using both Lambda and Flask.
Technologies: Python, Machine Learning, Predictive Modeling, TensorFlow, Pandas, Scikit-learn, Natural Language Processing (NLP), OpenAI GPT-3 API, Chatbots, Generative Pre-trained Transformers (GPT), Hugging Face, PyTorch, SQL, Amazon Web Services (AWS), Artificial Intelligence (AI), OpenAI GPT-4 API, Generative Pre-trained Transformer 3 (GPT-3), Data Science, Text Classification, Classification, Classification Algorithms, ChatGPT, OpenAI, Large Language Models (LLMs), Large Language Model Operations (LLMOps), Agentic AI, Agentic AI Systems, AI Agents, Prompt Engineering, Agentic Frameworks, AI Tools, Generative Artificial Intelligence (GenAI)
Assistant Director in Data Science and Machine Learning
2019 - 2023
EY
- Devised a classification model for imbalanced financial data that predicted whether a company is a good acquisition candidate using scikit-learn, imbalanced-learn, TPOT, and TensorFlow via the Keras interface.
- Improved the number of potential M&A clients by approximately 80% compared to the previous, personal experience-motivated approach.
- Deployed the model with Azure, Databricks, and MLflow.
- Collaborated with data engineers and DevOps to handle data correctly. Used SQL and PySpark to pull and format data from local and external sources.
- Formulated external data requests for the data manager.
- Validated the model with recall and F1 metrics. Employed cross-validation for further tests.
- Participated in regular meetings with stakeholders to formulate and reformulate the problem.
Technologies: Python, Scikit-learn, TensorFlow, Pandas, NumPy, SciPy, SQL, PySpark, Flask, Object-oriented Programming (OOP), Bash, Azure, Databricks, Git, Jira, Tree-Based Pipeline Optimization Tool (TPOT), Imbalanced-learn, MLflow, Machine Learning, Seaborn, AutoML, Data Science, Data Visualization, EDA, Client Presentations, XGBoost, Random Forests, Keras, Pytest, REST APIs, Testing, Artificial Intelligence (AI), Neural Networks, Deep Neural Networks (DNNs), Artificial Neural Networks (ANN), Deep Learning, Predictive Modeling, Data Modeling, Databases, Database Modeling, Data Analysis, Feature Engineering, Agile, Version Control, Spark, Data Analytics, Data Cleaning, Data Cleansing, Data Governance, Data Management, Python 3, Data Processing, ETL, Spark ML, Apache Spark, Classification, Classification Algorithms, Scenario Analysis, Statistical Modeling, Forecasting, Machine Learning Operations (MLOps), Hyperparameter Tuning, Model Evaluation, Model Validation
Online Tutor
2021 - 2022
University of London
- Tutored data analysis with Python employing Pandas, Matplotlib, Seaborn, and Scikit-Learn.
- Taught a theoretical course in artificial intelligence.
- Tutored the field of neural networks with TensorFlow and Keras. Tutoring involved assisting students with their technical queries while keeping close contact with a senior lecturer.
Technologies: Data Analysis, Data Visualization, Neural Networks, Machine Learning, Tutoring, Online Tutoring, Training, Jupyter, Jupyter Notebook, Python 3, Artificial Intelligence (AI), Data Science, Classification, Classification Algorithms
Data Scientist
2021 - 2022
University of Southampton
- Sped up the testing process in the first lab in the UK, where COVID-19 testing can be fully automated. We moved the lab from a prototype processing several hundred tests daily to 30,000—potentially increasing to 100,000 daily.
- Built a model (classification with scikit-learn, imblearn, and TensorFlow via Keras interface) that predicts positive or negative outcomes of a COVID-19 test.
- Developed SQL database solutions to store and retrieve data. Migrated data from legacy systems (local file systems) to new solutions (PostgreSQL and AWS), leading to significant performance improvements.
- Improved the existing Python codebase responsible for the automation of the laboratory information management system (LIMS) and data collection from the robots and biomedical professionals to support larger data volumes—up to 100,000 items per day.
- Contributed to the LIMS' back end and Flask app endpoints.
- Collaborated closely with testers and biomedical scientists to adjust the LIMS app and model to their changing requirements.
Technologies: Python, Pandas, SQL, Scikit-learn, TensorFlow, Linux, Bash, Flask, Git, Pytest, Imbalanced-learn, Machine Learning, Selenium, APIs, Object-oriented Programming (OOP), Data Science, Data Visualization, Matplotlib, Time Series, Jira, REST APIs, Testing, Artificial Intelligence (AI), Artificial Neural Networks (ANN), Neural Networks, Deep Neural Networks (DNNs), Deep Learning, Data Engineering, Data Analysis, Data Modeling, Databases, Amazon Web Services (AWS), PostgreSQL, Research, Automation, Software Development, Agile, Agile Software Development, Big Data, Cloud, Data Processing, Data Processing Automation, Version Control, Time Series Analysis, Feature Engineering, Data Analytics, Data Reporting, Scientific Data Analysis, Data Migration, Database Migration, Data Governance, Data Management, Python 3, ETL, Classification, Classification Algorithms, Healthcare Data Science, Bioinformatics, Healthcare Services, Healthcare IT, Hyperparameter Tuning, Model Evaluation, Model Validation, Machine Learning Operations (MLOps)
Online Lecturer
2020 - 2020
StackwisR
- Created several online courses in machine learning (regression, classification, clustering, deep learning, time series, marketing mix modeling, and computer vision) with Python.
- Filmed several online courses in machine learning (regression, classification, clustering, deep learning, time series, marketing mix modeling, and computer vision) with python.
- Included basic courses in NumPy, Pandas, Scikit-Learn, Matplotlib, and TensorFlow with Keras.
Technologies: Machine Learning, Python, Regression, Linear Regression, Pandas, Scikit-learn, TensorFlow, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Image Recognition, Computer Vision, Amazon Web Services (AWS), Neural Networks, Clustering, Classification, Data Visualization, Data Analysis, LaTeX, Videos, Recording, Tutoring, University Teaching, Training, Jupyter, Jupyter Notebook, Python 3, Classification Algorithms
Co-founder
2018 - 2019
EUCOIN
- Built an ecosystem to analyze the crypto exchange stream.
- Created algorithmic cryptocurrency and trading algorithms.
- Used machine learning to analyze cryptocurrency data.
Technologies: Python, Machine Learning, Regression, Pandas, Matplotlib, NumPy, SciPy, Scikit-learn, TensorFlow, Algorithmic Trading, Algorithmic Trading Analysis, Cryptocurrency, Bitcoin, Mathematics, Time Series, Object-oriented Programming (OOP), Flask, Pytest, Git, Data Science, Data Visualization, EDA, Data Analysis, Time Series Analysis, Statistical Methods, Seaborn, Testing, Trading, Arbitrage, Artificial Intelligence (AI), Predictive Modeling, Data Engineering, Data Modeling, Amazon Web Services (AWS), Data Governance, Data Management, Python 3, Data Processing, Amazon S3 (AWS S3), ETL, Blockchain, PySpark, Apache Spark, Forecasting, Time Series Forecasting, API Integration
Data Scientist
2017 - 2018
MC&C Media
- Built machine learning models (time series analysis via marketing mix modeling regression with scikit-learn) to analyze the performance of the clients' advertising and optimize their advertising budget.
- Created a data hub that now stores all the company and clients' data, making the analysis process easier using SQL, Python, and R.
- Collected and analyzed data from various sources (clients' databases) using exploratory data analysis (EDA) with SQL, Pandas, Matplotlib, and Seaborn.
- Collaborated closely with the marketing team and advertising consultants.
Technologies: Python, Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, Seaborn, PyBrain, SQL, R, Marketing Mix Modeling, Regression, Markov Model, Geolocation, Machine Learning, Linear Regression, Statistics, Hidden Markov Model, Statistical Methods, Statistical Significance, Statistical Analysis, Time Series, Time Series Analysis, Econometrics, Applied Mathematics, Data Visualization, Data Analysis, EDA, Pitch Presentations, Client Presentations, Data Science, Pytest, Artificial Intelligence (AI), Predictive Modeling, Data Engineering, Data Modeling, Marketing Attribution, Attribution Modeling, Google Analytics, Google Analytics API, B2B, Business to Business (B2B), Data Analytics, Data Reporting, Data Cleaning, Data Cleansing, Dashboards, Data Migration, Database Migration, Data Governance, Data Management, Python 3, Data Processing Automation, Forecasting, Scenario Analysis, Statistical Modeling
PhD Student
2012 - 2017
Royal Holloway
- Tutored all the university maths to year one, year two, and year three students. Tutoring included example classes, lecturing, and marking. Obtained a Teaching Commendation award for excellence in teaching in 2014.
- Created a mathematical model of magnetic skyrmions on Fourier lattice with Python.
- Deployed the mathematical model of magnetic skyrmions on Fourier lattice with AWS.
Technologies: Tutoring, University Teaching, Mathematica, Python, NumPy, SciPy, Pandas, Linear Algebra, Calculus, Computational Physics, Mathematics, Applied Mathematics, Fourier Analysis, Amazon Web Services (AWS), Linux, LaTeX, Training, Scientific Data Analysis, Scientific Computing, Python 3
Experience
Marketing Mixed Modeling for Advertising
I worked as a data scientist for a client's advertising analysis project. I collected data from various sources, such as the clients' CSVs, SQL databases, and public data. I formatted the data to single time series standards and conducted extensive data analysis to identify potential lag, adstock (carry-over effect), and diminishing returns.
To build the linear regression model, I performed feature engineering, hyperparameters tuning, and lag and adstock adjustments to ensure that the model accurately predicted the client's ROI. Once the model worked, I used it to answer clients' questions about ROI and provided them with actionable insights.
I regularly updated the model with new data to provide valuable long-term insights to the client. Through this project, I demonstrated my expertise in data analysis and statistical modeling and my ability to apply this knowledge to real-world business problems.
To build the linear regression model, I performed feature engineering, hyperparameters tuning, and lag and adstock adjustments to ensure that the model accurately predicted the client's ROI. Once the model worked, I used it to answer clients' questions about ROI and provided them with actionable insights.
I regularly updated the model with new data to provide valuable long-term insights to the client. Through this project, I demonstrated my expertise in data analysis and statistical modeling and my ability to apply this knowledge to real-world business problems.
Cryptocurrency Stream Analysis and Arbitrage Bot
I developed an algorithm that analyzed cryptocurrency streams from a crypto exchange (Binance), formatted the data, and suggested an optimal trading (arbitrage) strategy. The algorithm focused on BTC, ETH, altcoins, and USDT.
My responsibilities included collecting and formatting the data from various cryptocurrency streams to ensure the data was compatible with the algorithm. I then conducted extensive data analysis to identify trends and patterns in the data and used this information to suggest optimal trading strategies.
The algorithm was designed to identify arbitrage opportunities between different cryptocurrencies, including BTC (or ETH), altcoins, and USDT.
In addition to the aforementioned algorithm that analyzed cryptocurrency streams, I used LSTM to predict future rates of cryptocurrencies. By incorporating LSTM into the algorithm, I created a more sophisticated model that could make more accurate predictions based on historical data.
The LSTM model was trained on historical cryptocurrency data, allowing it to learn patterns and trends in the data. This information was then used to predict the future values of the cryptocurrencies, allowing for more informed trading decisions.
My responsibilities included collecting and formatting the data from various cryptocurrency streams to ensure the data was compatible with the algorithm. I then conducted extensive data analysis to identify trends and patterns in the data and used this information to suggest optimal trading strategies.
The algorithm was designed to identify arbitrage opportunities between different cryptocurrencies, including BTC (or ETH), altcoins, and USDT.
In addition to the aforementioned algorithm that analyzed cryptocurrency streams, I used LSTM to predict future rates of cryptocurrencies. By incorporating LSTM into the algorithm, I created a more sophisticated model that could make more accurate predictions based on historical data.
The LSTM model was trained on historical cryptocurrency data, allowing it to learn patterns and trends in the data. This information was then used to predict the future values of the cryptocurrencies, allowing for more informed trading decisions.
Recommendation System for a Building Company
As a data scientist, I developed a cutting-edge recommendation system based on clustering that suggested projects to existing clients. My responsibilities included collecting and formatting client data, performing feature engineering, and building a clustering model and recommendation system.
To begin the project, I collected and formatted client data to ensure compatibility with the recommendation system. I then conducted extensive feature engineering to identify key features that could be used in the clustering model.
Using the identified features, I built a clustering model capable of accurately identifying and grouping clients based on their needs and preferences. Once the clustering model was working, I suggested recommended projects to the existing clients based on the needs and preferences of similar clients in the cluster.
To begin the project, I collected and formatted client data to ensure compatibility with the recommendation system. I then conducted extensive feature engineering to identify key features that could be used in the clustering model.
Using the identified features, I built a clustering model capable of accurately identifying and grouping clients based on their needs and preferences. Once the clustering model was working, I suggested recommended projects to the existing clients based on the needs and preferences of similar clients in the cluster.
Job Search App
I created a powerful script that scraped major UK job boards and filtered for suitable data science contracts. The script was written in Python using the Selenium library, allowing efficient and automated web scraping. The script was designed to scrape job boards like Indeed and TotalJobs and filter for data science contracts matching specific criteria.
I incorporated NLP techniques to improve skills matching to further enhance the script's accuracy. By analyzing the job descriptions and identifying keywords related to data science skills, the script was able to identify suitable job postings that matched the skills and requirements of the client.
Once the suitable jobs were identified, they were added to the database for future analysis. This allowed for easier tracking of suitable job postings and ensured clients were quickly informed of potential job opportunities.
I incorporated NLP techniques to improve skills matching to further enhance the script's accuracy. By analyzing the job descriptions and identifying keywords related to data science skills, the script was able to identify suitable job postings that matched the skills and requirements of the client.
Once the suitable jobs were identified, they were added to the database for future analysis. This allowed for easier tracking of suitable job postings and ensured clients were quickly informed of potential job opportunities.
App to Find All Connections from Point A to Point B
I created a powerful app that allowed users to find all possible connections from one postcode to another. This included flights, trains, buses, and intercity connections, making it a comprehensive and valuable tool for travelers.
I collected and processed data from various sources, including APIs, Amadeus API, and web scraping using Selenium. This allowed for a wide range of transportation options in the app.
Although the app was initially developed as a prototype, there is potential to expand it and make it available to a broader audience. This would require further development and data collection, but the initial prototype provides a solid foundation for future work in this area.
I collected and processed data from various sources, including APIs, Amadeus API, and web scraping using Selenium. This allowed for a wide range of transportation options in the app.
Although the app was initially developed as a prototype, there is potential to expand it and make it available to a broader audience. This would require further development and data collection, but the initial prototype provides a solid foundation for future work in this area.
Education
2012 - 2016
PhD in Computational Theoretical Physics
Royal Holloway University of London - London, UK
2008 - 2012
Master's Degree in Theoretical Physics
University of Manchester - Manchester, UK
Skills
Libraries/APIs
Pandas, NumPy, SciPy, Matplotlib, Scikit-learn, TensorFlow, Imbalanced-learn, PySpark, PyBrain, XGBoost, Keras, REST APIs, Google Analytics API, Spark ML, PyTorch, OpenAI API, Llama API, Claude API, PyMC, JAX
Tools
LaTeX, Git, Mathematica, Pytest, Jira, Tree-Based Pipeline Optimization Tool (TPOT), Seaborn, MATLAB, gnuplot, Hidden Markov Model, AutoML, Amazon SageMaker, Jupyter, Google Analytics, ChatGPT, GitHub Copilot, Claude, Terminal
Languages
Python 3, Python, SQL, C++11, C++, Bash, R, YAML, Bash Script, Snowflake
Platforms
Ubuntu, Linux, Azure, Databricks, Amazon Web Services (AWS), Jupyter Notebook, Blockchain, Docker
Paradigms
Object-oriented Programming (OOP), Testing, Automation, Agile, Agile Software Development, B2B, ETL, Model Context Protocol (MCP)
Storage
Database Migration, Databases, Database Modeling, PostgreSQL, Amazon S3 (AWS S3), Redis
Frameworks
Flask, Selenium, Spark, Apache Spark, LightGBM, Agentic Frameworks
Industry Expertise
Bioinformatics
Other
Mathematics, Regression, Physics, University Teaching, Mathematical Modeling, Marketing Mix Modeling, Machine Learning, Linear Regression, Advanced Physics, Calculus, Quantitative Calculus, Statistics, Statistical Methods, Probability Theory, Differential Equations, Partial Differential Equations, Computational Physics, Eigenvectors, Linear Algebra, Mathematical Analysis, Applied Mathematics, Mathematical Programming, Matrix Algebra, Time Series, Time Series Analysis, Data Visualization, Data Analysis, EDA, Data Science, Artificial Intelligence (AI), Neural Networks, Deep Neural Networks (DNNs), Artificial Neural Networks (ANN), Predictive Modeling, Large Language Models (LLMs), Agentic AI, Agentic AI Systems, Deep Learning, Data Migration, Data Governance, Data Management, Computational Biological Physics, Markov Model, Geolocation, MLflow, Algorithmic Trading, Algorithmic Trading Analysis, Cryptocurrency, Bitcoin, Quantum Computing, Stochastic Differential Equations, Computational Biology, Fluid Dynamics, Electrodynamics, Complex Networks, Statistical Significance, Statistical Analysis, Econometrics, Pitch Presentations, Client Presentations, Random Forests, APIs, Trading, Arbitrage, Data Engineering, Cross-selling, Clustering, Recommendation Systems, Data Modeling, Natural Language Processing (NLP), Image Recognition, Computer Vision, Classification, Videos, Recording, Tutoring, Online Tutoring, Fourier Analysis, Training, Research, Software Development, Big Data, Cloud, Data Processing, Data Processing Automation, Version Control, Feature Engineering, Marketing Attribution, Attribution Modeling, Business to Business (B2B), Data Analytics, Web Scraping, Amadeus, Data Reporting, Generative Pre-trained Transformers (GPT), Data Cleaning, Data Cleansing, Amazon RDS, Scientific Data Analysis, Scientific Computing, Dashboards, OpenAI GPT-3 API, Chatbots, Hugging Face, OpenAI GPT-4 API, Generative Pre-trained Transformer 3 (GPT-3), Data Scraping, Text Classification, Classification Algorithms, OpenAI, Large Language Model Operations (LLMOps), Forecasting, Bayesian Machine Learning, Open-source LLMs, Meta Llama, Llama 3, LangChain, Azure Databricks, FastAPI, Time Series Data, Time Series Forecasting, Predictive Analytics, Statistical Modeling, Scenario Analysis, Demand Forecasting, Healthcare Data Science, Light LLMs, Cursor AI, Opik, RAG Systems, RAG Pipelines, RAG Architecture, Agentic RAG Systems, Retrieval-augmented Generation (RAG), AI Agents, AI Compliance Agents, Vector Databases, Prompt Engineering, Benchmarking, Agentic Coding, Agentic Workflow Design, AI Tools, AI Testing, Generative Artificial Intelligence (GenAI), Anthropic, ChatGPT API, ChatGPT Prompts, YAML Pipelines, Agent Evaluation, API Integration, Optical Character Recognition (OCR), Bayesian Inference & Modeling, Marketing Science, Marketing Mix, Probabilistic Modeling, Bayesian Statistics, Regression Modeling, A/B Testing, Multivariate Statistical Modeling, Logistics & Supply Chain, Supply Chain, Supply Chain Optimization, SAP Supply Chain Management (SCM), Inventory Management, Inventory, Sales Forecasting, Gradient Boosting, Decision Trees, lightgbm, Healthcare Services, Healthcare IT, K-means Clustering, Clustering Algorithms, Google Meridian, Causal Inference, CRM, ROI, Incrementality Testing, Marketing Analytics, Data Analytics (Marketing), Analysis of Variance (ANOVA), Funnel Marketing, Hyperparameter Tuning, Model Evaluation, Model Validation, Machine Learning Operations (MLOps), Xarray, SSH, AWS SSH Keys, DuckDB
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