Data Analytics & Machine Learning

Bridging statistical modelling, automation, and applied machine learning to drive business insight.

About Me

Cem Karisli

Cem Karisli

MSc Financial Data Analytics | University of Sussex

2 Corporate Roles
2 Master's Programs
I am currently pursuing my MSc in Financial Data Analytics at the University of Sussex, while completing my thesis in Computer and Information Science at Dogus University.
I focus on Python, SQL, and statistics to deliver practical outcomes such as KPI reporting, automation, and decision-ready analysis.
I have worked in business analytics, customer experience, and process improvement at companies like MediaMarkt and Koctas, collaborating with cross-functional teams in agile environments.
Right to work: UK Student visa (term-time restrictions apply). Planning to switch to the Graduate visa after graduation.

Education

Sep 2025 – Aug 2026 (Expected)

MSc Financial Data Analytics

University of Sussex, United Kingdom

Focus: Probability, statistics, optimisation, and algorithmic data science with Python-based financial analytics.

Active
Sep 2024 – Present

MSc Computer and Information Science

Dogus University, Istanbul

Status: Thesis stage
Coursework: Machine Learning, Deep Learning, Computer Vision, Business Intelligence

Thesis Stage
Sep 2017 – Aug 2023

BSc Industrial Engineering

Cukurova University, Adana

Focus: Operations Research, Optimisation, Statistical Quality Control, Production Planning

Completed

Experience

Insurance & Value-Added Services Specialist (Data & Reporting Focus) | MediaMarkt

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Reporting automation and performance analytics using Python and advanced Excel.

  • Automated recurring business reports with Python and advanced Excel workflows.
  • Supported data extraction and analysis to improve reporting speed and consistency.
  • Worked in cross-functional agile teams; contributed to delivery tracking and stakeholder updates.

Jan 2025 – Jun 2025

Customer Experience Analyst | Koctas

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KPI monitoring and process improvement across contact center and customer experience operations.

  • Tracked and reported KPIs such as NPS and operational call metrics; delivered actionable insights.
  • Built Excel dashboards and performed root-cause analysis for performance drivers.
  • Supported testing and improvement initiatives with internal stakeholders and vendors.

Jan 2023 – Jan 2024

Projects

Cricket Analytics (Industry-Academic Collaboration)

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Built baseline expected runs per ball (xRuns) models to support performance evaluation and decision-making.

  • Modelled ball-level outcomes using interpretable regression baselines for decision-ready metrics.
  • Produced player-level residual leaderboards with uncertainty estimates (standard errors and confidence intervals).
  • Delivered report-ready outputs for non-technical decision-makers.

Metric Documentation for ML Classifiers

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Interactive Streamlit app visualising and comparing Log Loss and Focal Loss metrics for machine learning classifiers.

  • Explained the mathematical intuition and practical use-cases for both metrics.
  • Developed interactive charts for exploring metric behaviour across scenarios.
  • Deployed on Streamlit Cloud: log-focal-loss.streamlit.app

Pneumonia Detection from X-rays

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CNN-based application for pneumonia detection from chest X-ray images, built for a graduate Computer Vision course.

  • Implemented a deep learning classifier and evaluated model performance on imbalanced data.
  • Created a simple desktop GUI for uploading X-rays and viewing predictions.

Appointment Availability Notifier

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Automated monitoring script that checks availability and sends alerts via Telegram to reduce manual effort.

  • Built a Python monitoring workflow and notification pipeline.
  • Designed it to run on a schedule and send near real-time alerts.
  • Focused on reliability and clean logging for maintainability.

Articles

Detecting Credit Card Fraud with Anomaly Detection

Overview of anomaly detection algorithms for identifying fraudulent transactions with machine learning.

Why a Low Pearson Correlation Doesn’t Mean “No Relationship” in Business Data

Exploring why a low Pearson correlation doesn’t always mean variables are unrelated, and how nonlinear business data can mislead analysis.

Fast EDA on Cricket Data with Y-Data Profiling

A fast way to perform Exploratory Data Analysis using Y-Data Profiling in Google Colab, and why interpretation still matters.

Skills

Programming

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Core languages and daily tooling.

Python
SQL
R (basic)

Data and Machine Learning

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Analysis and modelling capabilities.

Pandas
NumPy
Scikit-learn
Feature Engineering
Model Evaluation
Statistical Modelling

Visualisation and BI

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Communicating insights clearly.

Excel (advanced)
Power BI
Matplotlib
Reporting Automation

Tools and Platforms

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Workflows, collaboration, and data access.

GitHub
SAP BO
Azure (basic)
Linux (basic)

Contact