Hire the Best Quantitative Analysts
in Greece

📊 I hold a PhD in Economics with expertise in empirical econometrics, complemented by five years of professional experience in economic analysis and data analytics. My background also includes extensive work in time series forecasting and machine learning applications. 📈 I have collaborated with professionals across the stock market and betting industries to deliver projects forecasting stock market indices and soccer outcomes. 🛠 Technical Skills & Expertise: ✅ Data Analysis: Proficient in data processing, cleaning, and exploratory data analysis using pandas and numpy to derive actionable insights. ✅ Data Visualization: Skilled in effectively communicating findings through tools such as matplotlib, seaborn, and interactive tools such as Streamlit. ✅ Machine Learning: Experienced in creating Machine Learning forecasts and reports using various packages, including scikit-Learn, keras, and statsmodels. ✅ Econometrics: Demonstrated academic success in empirical econometric projects, specializing in causal inference with panel data and employing models such as propensity score matching and difference-in-differences, as well as time series forecasting models (ARIMA/VAR). ✅ Research: Experienced in conducting rigorous empirical research in economics, applying statistical and econometric methods to analyze large datasets. Skilled in extracting meaningful insights and translating them into actionable economic conclusions. Published in peer-reviewed journals on firms' competitiveness. Also experienced in academic writing and research dissemination.

Seasoned data scientist and quantitative developer, focused on building and evaluating complete trading infrastructures (from strategy/setup inception to thorough backtesting to live automated trading system) for funds, family offices and top-tier retail traders. Former founder and CTO of Quantflare, an alternative data provider startup with a focus on the algorithmic trading market subset. Few of the items I can assist you at : - Signal & Factor Analysis & Robustness Testing - Thorough Backtesting and Strategy Performance Reporting/Evaluation - Systematic Portfolio Optimization - Systematic Hedging - Automated Signal Research & Evaluation - Strategy Incubation & Live Deployment Constantly seeking for further alpha and/or business opportunities - happy to chat with experienced individuals in the industry.

Currently working as data analyst in Seatchatlas and Statbid. With a strong academic foundation and extensive professional experience in business intelligence, data visualization, and statistical modeling, I am confident in my ability to contribute effectively to any organization. After earning my degree in Finance, where I gained a solid grounding in Finance, Statistics, Macroeconomics, and Marketing, I pursued a Master's in Business Informatics. Most recently, I completed an MSc in Complex Systems and Networks, where I extensively used R to graphically represent statistical models and complex networks. These academic pursuits have honed my analytical and technical skills, while fostering a deep appreciation for the interplay between Computer Science and Economic Sciences. Currently, I am advancing my expertise through a PhD program, focusing on the application of machine learning and network science techniques in financial and economic systems. My research involves the development of predictive models and causal inference frameworks, using large-scale data analysis to address complex, real-world challenges in financial markets and organizational behavior. This experience has deepened my proficiency in quantitative methodologies, reinforced my ability to handle interdisciplinary projects, and enhanced my problem-solving approach when working with intricate datasets. In my professional experience as a Business Intelligence Analyst, I specialize in transforming complex datasets into actionable insights. My work involves leveraging tools such as R/Shiny, Tableau, Qlik Sense, and Microsoft Power BI to deliver data-driven solutions that align with business objectives. Additionally, I have collaborated with various firms on freelance projects, further refining my expertise in data visualization and statistical modeling. As an author for R-exercises, I have published tutorials and exercises on R packages like shiny, ggplot2, ggvis, and Rmarkdown, as well as machine learning methodologies. Through these contributions, I have demonstrated my ability to break down complex concepts into accessible and practical solutions, benefiting both professionals and learners in the field. My diverse academic and professional background has equipped me with a unique perspective, blending precision, creativity, and technical expertise. I am passionate about solving complex problems, uncovering actionable insights, and driving data-informed decision-making. I am excited about the possibility of contributing to impactful projects and would be delighted to discuss how my background aligns with organizational needs. Attached you can find URLs of some of my Shiny projects. There are only parts of them that I had clients' permission to deploy and use as a portfolio.

PhD in Macroeconomics & Finance with 7+ years of research and real-world analysis. I specialize in making complex economic trends, central bank policies, and financial market dynamics clear, actionable, and relevant for clients in finance, business, and policy sectors. With proven experience in academic research, economic policy analysis, and financial markets, I deliver data-driven insights, forecasts, and reports tailored to your needs. I’ve published my work internationally, tutored university courses in capital markets and economics, and collaborate globally as an independent analyst. If you need clear, reliable economic analysis or tailored financial insights to support smart business decisions, let’s work together!

A Data Scientist with Nuclear Physics background and strong problem solving skills. Using Data Science techniques to improve core business KPIs and provide Business insights. Classification of problems may include geospatial data processing, customer churn rate prediction and prevention, fraud detection, Customer Segmentation and more. Building Machine Learning models and visualize results. Core skills: Machine Learning, Data Mining, Quantitative Analysis, Mathematical Modelling, Statistical Analysis, Data Analysis, Fraud fighting. Tools: SQL, R, Python. Big Data: Hive, Hadoop, Zeppelin, Hue, Presto. Visualization: Tableau, CartoDB, MapBox, R and Python libraries. Using AWS infrastructure and/or local VMs, GitHub, Phabricator and JIRA.