Data Scientist · Necromancer
Burak Suyunu
Builds machine learning and optimization models, bringing methods from research into production.
Co-founder of Lumtify, and the one who came up with the name. Shuttles between academia and industry: research assistant at Boğaziçi University, NLP data scientist on Trendyol’s data science team, and now a PhD candidate in bioinformatics. Knowing all three worlds from the inside means a paper read in the morning can be code by the evening, and which method suits which problem is usually clear from the start.
A model that merely “works” is not satisfying; the experiment gets set up one more time until “is this really the best version?” has an answer. Worked on model development for the Çimsa and MMK optimization projects. Alias: Necromancer. Bringing back the data set nobody has any hope for, or the model that was set aside, is usually this job.
Projects
Çimsa
Çimsa S&OP: from global network optimization to the fuel procurement decision
We brought Çimsa's production, logistics and sales network across 15 locations into a single mixed-integer model, then built a second model that turns the resulting clinker production plan into a fuel recipe and procurement decision.
- 10% ↓ Inventory cost
- 3% ↓ Manufacturing, warehousing, distribution cost
MMK
MMK: line balancing and order–raw material matching in flat steel production
An optimization system that matches open orders to the right raw material while respecting production line capacity and every stage of the bill of materials. Built across three phases.
- 4,000+ Open sales orders
- ~9 Raw material candidates per order
Kuveyt Türk
Kuveyt Türk: master–apprentice mentoring for the Digital Marketing and Retail Customer Analytics teams
It began in 2023 with SQL, Power BI, Python and machine learning training for Digital Marketing's analytics team. The team then built its own projects under Lumtify's mentoring: digital score, target management, churn cause analysis, UI usage analysis, product recommendation and churn prediction models. Since 2025 the work has continued with the Retail Customer Analytics team on spending analytics, a luxury spend score and next-best-action prioritisation. The team writes the code; Lumtify sets direction, brings the method and checks the output.
- Master – apprentice Model
- 8+ Digital projects
Kalekim
Kalekim: from MVP to a forecast management system in three years
We started with a machine-learning MVP for two product groups fed from Excel; in year two it became a forecasting process retrained every month for every product, and in year three the Kalekimforecast application that planners run themselves. The forecast is now an input to Kalekim's supply, production and logistics planning.
- 2 groups → all Scope
- 3 Encoding methods
Hayat Kimya
Hayat Kimya: weekly e-commerce order forecasting on Microsoft Fabric
A system that forecasts marketplace and e-commerce orders weekly, at the granularity the warehouse shift plan needs, and runs as a single Fabric pipeline from data warehouse to report.
- Weekly Forecast horizon
- One pipeline Automation
Türkiye Finans Participation Bank
Türkiye Finans: training and project mentoring for the HR analytics team
In 2024 we spent one day a week with the participation bank's HR Analytics team: first Python and machine learning training, then mentoring on the team's own projects. The relationship between the recruitment assessment inventory and performance and retention was analysed by job family, and a literature review and model design were done for employee attrition prediction. The team applied what it learned to the institution's own data straight away.
- 1 day a week Format
- Python · ML Training
Kale Seramik
Kale Seramik: a demand forecasting PoC we did not continue
An MVP that started on the same roadmap as Kalekim: two product functions, 34 methods, three encoding approaches. The result fell short of the target and the second phase was not started. This page explains what did not work, and why.
- 141 DFUs
- 200+ Product attributes
Banks Association of Türkiye (TBB)
TBB: data science training for bankers
Within the Banks Association of Türkiye's training programme, a 62-hour, 21-day programme called the 'Data Analytics Journey' for bank employees who use data in their work: introduction to data science, SQL, Python, statistics and EDA, visualisation, machine learning, industry cases. Sessions ran in the evenings, every module had homework and the hands-on work was done in Colab. TBB scored every module out of 10 in its participant survey; the average came out above 9.
- 62 hours Duration
- 9+ / 10 Evaluation