Work

Built search, image classification, and similarity recommendation systems. Developed product search on titles and description metadata without exact match requirements, and a mixture-of-experts style image classifier across 50+ subcategories. Deployed interactive Streamlit platforms and FastAPI backends.

Developed fintech/payment ML tools including price/quote prediction, churn prediction, lifetime value analysis, and financial forecasting apps. Designed production pipelines deployed on Azure using Docker and Kubernetes, working with collaborative git pull request workflows.

Created demand, revenue, and call-center forecasting systems integrated directly into their e-commerce SaaS. Deployed desktop Tkinter MLStudio apps and Streamlit business apps backed by inference APIs.

Designed a broad business intelligence and analytics system for e-commerce distributor channels. Features include market basket association rules (FP-growth, Apriori), RFM metrics, anomaly detection, sales forecasting, and customer clustering.

Constructed concrete strength regression applications, patient churn models, lifetime value estimations, and customer segmentation reports. Shipped multiple Tkinter MLStudio applications and Streamlit dashboard tools.

Conducted BERT-based HR candidate identification feasibility, created a customer database-connected churn/LTV tool, email segmentation pipelines, and mentored mid-level developers on building a Flask MLStudio application.

Served as Senior ML consultant at the R&D center. Built Basal Metabolic Rate (BMR) prediction models for health sector clients, e-commerce campaign-centric customer clustering (K-Means, DBSCAN, CLARA), and drone-based bird detection algorithms, while leading technical proposal processes for ITEA projects.

Developed operational demand forecasting engines and API integrations for Hesapcini pre-accounting SaaS and WMS solutions.

Designed churn prediction, customer lifetime value models, and demand forecasting backend APIs for SME finance SaaS platforms.

Modeled organic marketing and network traffic forecasting, presenting forecasts via a customized Streamlit end-user dashboard.

Built financial forecasting workflows for CRM and SME business-management software, utilizing Tkinter and Streamlit.

Created bid quote prediction models and automated sales forecasting pipelines, utilizing Tkinter model-builders and Streamlit web applications.

Engineered import/export logistics analytics, customer churn classification, and CLTV regression, successfully securing TÜBİTAK 1511 grant funding.

Mentored and led a team of five data scientists and engineers on developing large-scale restaurant demand forecasting algorithms using time-series and gradient boosting.

Built automotive export demand forecasting engines for steering and suspension spare parts. Shipped a custom Tkinter MLStudio application managing 10-20 distinct model configurations.

Pioneered call-center call volume and handling time forecasting pipelines. Implemented an autoencoder-based anomaly detection workflow to scrub historical training data.

Developed a retail demand forecasting system for new products using CNN-based image similarity matching to historical product data, mapping forecasts based on style resemblance.

Conducted merchant transaction behavior clustering, customer churn analysis, and MLP modeling, publishing results via Streamlit prototypes.

Mentored three mid-level data scientists on machine learning, leading the development of a desktop MLStudio forecasting workbench.

Provided corporate training on machine learning and deep learning, guiding internal developers on building a custom C# MLStudio tool for regression and forecasting.

Scoped and drafted R&D Center registration and TÜBİTAK grant applications. Designed algorithms for listing recommendations, rental valuation, and duplication checking using CNN image matching.

Modeled customer churn and demand forecasting feasibility frameworks for various SME technology services.

Generated demand and sales forecasting pipeline reports for retail operations.

Designed and delivered an intensive 8-module machine learning and deep learning curriculum for industrial engineering teams.

Conducted demand forecasting feasibility analysis for restaurant supply chains, alongside training courses for engineers.

Researched energy/SCADA and weather-based load forecasting feasibility using Extreme Learning Machine (ELM) networks.

Developed app store download and ranking prediction models using minimum Redundancy Maximum Relevance (mRMR) feature selection.

Created LSTM deep learning time-series models for energy forecasting and imbalance cost reporting.

Produced feasibility reports on revenue model analysis, incorporating customer clustering and mRMR feature selection.

Conducted fraud modeling evaluation, creating classification testbeds and metrics reports for risk analysis.