🧩 Currently working as an embedded data scientist in the product information squad, leveraging traditional ML frameworks as well as Generative AI to enrich and improve the quality of text data used in the front-end of our online store. Laying the foundation for MlOps best practices and data lineage by using DBT, MlFlow and model monitoring frameworks.🧩 Lead the scoping, development and productionisation of a novel recommender system solution which serves 4M+ users daily, leveraging LightGBM to predict high-demand products combined with an existing personalisation solution. Employed MlFlow for experimentation and hyper-parameter tuning, and established a reusable pipeline with VertexAI for end-to-end model training.🧩 Owner and maintainer of a weekly personalisation campaign solution. Conducted a full review to address criticalities in data quality and process issues, proposing and obtaining buy-in for a new solution architecture, which reduced incidents and related ad-hoc interventions by the team by more than 90%.

Developing and maintaining digital media measurement pipelines to track their incremental value Overseeing successful delivery of channel attribution measurement with Markov Chain

 Tutored students in the course of Machine Learning and Data Mining Assisted students on both theoretical and practical applications of ML methods

 Processing of applications and maintenance of database records Assisting with student enquiries and enrolment procedures

 Worked on the back-end for projects related to education quality analytics Developed an algorithm for detection of abusive comments in student surveys Back-end data processing for online dashboards of survey results

 Guided tours of the Museum for the general public and schools Hands-on activities related to science and technology