End-to-end NLP models to get insights from the employee feedbacks continuously

Natural Language Processing (NLP), an ever-growing interest area in the analytics community, creates big impacts on HR/People Analytics domain. It enables transformation of unstructured data into valuable insights with greater operational speed and accuracy to support organizational decision mechanism. During my talk, I will address NLP use cases in HR field and how to build end-to-end NLP models (topic and sentiment model) on survey data to get insights from the employee feedbacks continuously by touching on each stage of model life cycle.  

• NLP business use cases in People Analytics at ING
• Deep dive topic and sentiment model on survey data
• Full life cycle stages of the NLP models (including data ingestion, model development, deployment, monitoring and validation)

———————————————————————————————————————————————————–

Büşra Çıkla – Data Scientist | ING

Busra is an enthusiastic data scientist with passion for analytics at ING’s People Analytics Team under HR in Amsterdam. She has gained considerable international experience on designing and developing end-to-end advanced analytics solutions to a business problem in three different teams within the organisation during the last 4 years. Some of the projects which she has worked on so far are resource optimisation for call center and collection processes, churn for wholesale banking customers, name matching for KYC processes, pricing projects for retail customers. At her current team, she is responsible for NLP models and ad-hoc analysis on survey data to help the organisation have a healthy working environment. Busra has a background on optimisation and operational research from her B.Sc. study and she is currently pursuing her M.Sc. degree on Data Science, thesis stage