Hi, I'm Alexandros Polyzoidis.
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Skilled in building scalable solutions for e-commerce and logistics. Proficient in Python, SQL, Azure, and deep learning frameworks. Passionate about solving real-world problems through data-driven insights and model deployment.
About
I am a Data Scientist skilled in machine learning, cloud services, and building impactful solutions for e-commerce and logistics. My expertise includes Python, SQL, PowerBI, and Azure tools like Data Factory, Synapse Analytics, and Databricks. I have experience with projects in customer segmentation, credit card fraud detection, and image classification for damaged goods. My work has focused on NLP and computer vision, helping to develop scalable solutions that address real-world challenges.
- Quantitative Analysis: Mathematics, Statistics
- Programming Languages: Python, R, SQL
- Machine Learning & Deep Learning: Scikit-learn, PyTorch, TensorFlow, Keras, etc.
- Data Visualization: Tableau, Power BI, ggplot2, matplotlib, seaborn
- Cloud & Big Data: Azure ML Studio, Synapse Analytics, Databricks, Data Factory
- Tools & Software: GitHub, Jupyter, RStudio, VS Code, PgAdmin, Postman
- Soft Skills: Critical Thinking, Communication, Collaboration
Currently, I am working as a freelance data scientist and seeking an opportunity to join a data science or machine learning team where I can contribute, learn, and grow in a dynamic environment.
Experience
- Developed and implemented customer segmentation models for e-commerce clients, enhancing targeted marketing and personalization strategies.
- Created computer vision solutions for supply chain and logistics companies, including image classification to detect damaged loads during loading and unloading processes.
- Leveraged Python, Scikit-learn, and Azure ML Studio to build scalable models; utilized Tableau and Matplotlib to present insights effectively.
- Tools: Python, Scikit-learn, Tensorflow, Keras, Pytorch, Azure ML Studio, Tableau, Matplotlib, PowerBI
- Conducted exploratory data analysis and applied K-means clustering to segment prospective clients and prioritize leads, contributing to an annual revenue growth nearing €400,000.
- Utilized Tableau and Power BI to visualize key sales metrics, generate actionable insights, and inform strategic decisions, optimizing outreach efforts.
- Leveraged Odoo CRM to manage daily activities, accurately forecast pipelines, and develop data-driven quotes and proposals.
- Tools: Tableau, Power BI, Odoo CRM, Python, K-means Clustering
- Enhanced client relationships by implementing data-driven strategies, resulting in a 20% increase in client satisfaction ratings.
- Utilized Entersoft CRM software to track client interactions and identify trends for targeted strategies.
- Collaborated with the analytics team to analyze sales data and provide personalized recommendations to clients.
- Tools: Entersoft CRM, Data Analytics, Client Relationship Management
- Coordinated and planned transport activities for the Packed-Goods Transport Department, increasing on-time delivery rates by 15%.
- Minimized empty cargo trips by 20% by strategically reorganizing truck schedules based on customer demand and inventory levels.
- Streamlined route planning for 50+ vehicles using GPS and optimization software, boosting delivery efficiency by 25% and cutting fuel use by 15%.
- Tools: GPS, Route Optimization Software, Data Analytics, Excel
Projects

An end-to-end AI application that classifies visitor reviews into positive, neutral, or negative sentiments.

A deep learning model to identify endangered animal species based on image classification.

A deep learning project that classifies the emotion (Happy or Sad) in uploaded images.

A deep learning model in PyTorch designed to classify fraudulent credit card transactions.

An image classification project that identifies different cards in a deck using PyTorch.

A predictive model using machine learning techniques to estimate passenger survival likelihood on the Titanic.

A sequence model built with LSTM to classify handwritten digits from the MNIST dataset.

A machine learning model to classify soil types based on physical and chemical soil properties.

An analysis project to explore visitation data and trends across different countries.

Uses regression models to predict the age of abalones based on physical measurements from a dataset.
Skills
Quantitative Analysis and Programming Languages






Libraries





Frameworks





Cloud & Big Data




Development & Deployment Tools




Tools & Software






Soft Skills



Education
Patra, Greece
Degree: Master of Data Science & Machine Learning
- Machine Learning & Data Mining
- Deep Learning for Computer Vision
- Statistical Data Analysis
- Big Data Technologies & Cloud Computing
- Natural Language Processing
Relevant Courseworks:
Aristotle University of Thessaloniki
Thessaloniki, Greece
Degree: M.Sc. in Logistics & Supply Chain Management
- Supply Chain Analytics
- Inventory & Warehouse Management
- Logistics Optimization Techniques
- Transportation Planning & Operations
- Quantitative Methods in Supply Chain
Relevant Courseworks:
Aristotle University of Thessaloniki
Thessaloniki, Greece
Degree: B.Sc. in Finance
- Financial Management
- Corporate Finance
- Investment Analysis
- Accounting Principles
- Economics & Quantitative Methods
Relevant Courseworks:
CERTIFICATIONS
- Microsoft Certified: Azure Data Scientist Associate – Microsoft, 2024
- Python A-Z™: Python for Data Science with Real Exercises! (Udemy)
- 15 Days of SQL: The Complete SQL Masterclass 2024 (Udemy)
- Tableau A-Z: Hands-On Tableau Training for Data Science (Udemy)
- R Programming A-Z: R for Data Science with Real Exercises (Udemy)