Case Study · Finance
Türkiye Finans: training and mentoring for the HR analytics team — from recruitment assessments to employee attrition
One day a week in 2024 with the participation bank's HR Analytics team: 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; a literature review and model design were done for employee attrition prediction. The method learned was applied to the institution's own data at once.
- 1 day a week
- Format
- Python · ML
- Training
- 2
- Projects
- Master – apprentice
- Approach
Training + mentoring, six months
Assignment-driven, with exams
Recruitment inventory · attrition
The team does the analysis
Context
Türkiye Finans’s Human Resources Analytics team wanted to make better use of its technical skills. From the “data analytics journey” catalogue Lumtify Academy prepared for banks — introductory, foundation, reinforcement and advanced trainings — a path specific to HR analytics was drawn, and the engagement combined training and mentoring on the same calendar: one day a week, six months.
The roadmap was classic: meetings with managers and goals, one-to-ones with the team, assessing competence levels, an inventory of tools and data, identifying the target project for mentoring, the trainings needed, starting the project.
Training
Weekly Python training from February: language fundamentals, assignment-driven progress, examples from the team’s current work. Machine learning in May: preparing data for a model, feature engineering, model validation, regression and tree-based methods. The training was measured with an exam and closed with feedback.
Project 1 — Recruitment inventory analysis
The bank uses a comprehensive assessment tool in recruitment, covering candidates’ general knowledge, aptitude and personality inventories. This data, accumulated over years, had never been related to the later performance of those hired or to whether they stayed at the bank.
The analysis was done by job family — retail banking, call centre, information systems, branch support, digital banking, loans, relationship management roles. In each job family: which inventory dimensions relate positively or negatively to performance; a t-test comparison of dimension means between leavers and stayers; the effect of the university attended. The result was concrete, job-family-specific findings on which dimension to look at for which role in the hiring decision — and, in some roles, relationships that ran against expectation.
The team did the analysis and the team prepared the presentation; Lumtify was alongside on method and interpretation.
Project 2 — Employee attrition
The second project was a model predicting the probability of an employee leaving: a literature review and screening, identification of the data needed, model design. The findings of the inventory analysis became feature candidates for this model.
Outcome
At the end of six months the team was one that could apply statistical tests and machine learning on its own data and give its own presentations. That was the reason for running training and project with the same team in the same period: a method learned should be applied at once to the institution’s own question.