Prenil Sewmohan

Led and mentored a team of data scientists across multiple business units in the banking, insurance, property, and retail industries, driving business strategy and technical innovation. Played a pivotal role as part of the core strategic team leading the cross-business analytics function, influencing the management team’s decisions on the strategy and direction of the property business. Developed an AI-powered Retrieval-Augmented Generation (RAG) Python website to facilitate search, document querying, and knowledge building on Lightstone’s products, enhancing user access to and understanding of complex datasets and documentation. Contributed to the strategic initiative to consolidate and formally document Lightstone’s products, supporting a shift towards a product-led business model that improved internal alignment and market positioning. Prioritized and translated business goals into technical objectives, leading to the creation of actionable OKRs and backlogs for data science and technical teams. Designed and deployed recommendation and propensity models, as well as geographical clustering models, providing analytical insights and value-added services to users in the property platform ecosystem. Built internal web applications to explain machine learning model predictions, improving adoption, query handling, and comprehension among clients and commercial teams. Established an internal technical blog aimed at knowledge sharing, team building, and fostering a strong sense of identity within the data science team. Represented Lightstone at industry knowledge-sharing sessions, delivering property insights to key partners in the banking sector.

Assistant lecturer in the department of statistics

Nanodegree program covering the fundamentals of artificial intelligence. This includes topics such as informed and uniformed searches, adversarial agents and genetic algorithms. The Nanodegree includes four larger practical projects reviewed by the very able and professional reviewers at Udacity. These projects give the student a chance to apply the concepts learnt to practical problems and to get their hands dirty with the actual coding too. Some specific algorithms covered are depth-first, greedy, breath-first and A* searches as well as the minimax algorithm, alpha-beta pruning and iterative deepening.

Online nanodegree course teaching the fundamentals of machine learning. This includes a good grounding in the mathematics and statistical concepts underlying the machine models themselves. Supervised learning, unsupervised learning, reinforcement learning and deep learning are all part of the syllabus and each section includes practical exercises and projects in the Python environment. There is also a final capstone project in which the student finds their own ML problem and completes an entire project demonstrating what they have learnt.