In this role am dedicated to applying data intelligence across product development, marketing, and service offerings both internally and for our business clients. My role involves leading and facilitating the implementation of innovative data solutions.My notable achievements in this role include:- Orchestrating the development of a subscription platform designed for business data visualization, enabling the tracking of relevant Key Performance Indicators (KPIs).- Leading the collection of sensor device data and the creation of an analytics solution for energy monitoring, utilizing commercial building energy consumption data to derive insights to construct occupant profiles, track appliances' energy use via disaggregation, and help businesses cut energy costs.- Conducting training sessions and integrating custom machine and deep learning algorithms for a variety of business intelligence tasks.- Taking charge of the company's data academy as the lead instructor, where I have trained and interacted with over 300 early career professionals who now work across 3 different continents.-Generating digital content such as blog posts, tutorial videos, and case studies centered around Data Science.

I work within a high-impact scientific research environment at the University of East Anglia in the U.K. focusing on clinical trials, data collection, aggregation, and algorithm development for a medical wearable device. Within this role, I worked directly with the medical wearable device and constructed data processing pipelines to process health data, utilizing signal processing and deep learning modeling techniques.In addition, I developed a medical image annotation software application. This tool streamlines the clinical annotation process for medical images, fostering collaboration within our interdisciplinary research team while ensuring data accuracy and validation.The medical wearable device is a pivotal aspect of our work, as it provides continuous monitoring of eye movements in patients' homes. This data allows us to gain crucial insights into physiological parameters during dizziness attacks, aiding in the understanding and diagnosis of vestibular disorders.We anticipate that the adoption of the device by the U.K NHS will lead to expedited and more precise diagnoses for patients. This, in turn, will facilitate prompt access to effective treatments, significantly improving patient outcomes.

This is a UK UKRI-funded research project aimed at improving the robustness of algorithms in the intraday energy trading market. This project required data gathering and enhancing the forecasting accuracy of existing machine learning models for day-ahead 24hrs electricity price prediction using real-time and historical data from the Nord Pool Intraday market, where market participants trade continuously, 24 hours a day, with delivery on the same day. I collaborated with a team of 5 researchers at the School of Computing Sciences University of East Anglia and conducted a thorough examination of research papers to identify diverse time series models with objectives relevant to the commercial project and I developed state-of-the-art time series forecasting models for predicting day-ahead 24hrs electricity prices within the energy auction market with noticeable improvement compared to exiting models.

In this role, my responsibilities encompassed overseeing business development initiatives and managing various social media platforms. Collaborating closely with the marketing team to meet the needs of our business clients.