Rashmi has over ten years of experience in the field of statistics and data analysis (structured and unstructured), incorporating a broad spectrum which includes conventional and innovative quantitative modelling.
Rashmi has both strong theoretical and applied expertise, with a deep specialism in probabilistic quantitative modelling using mathematical Artificial Intelligence techniques known as Artificial Neural Networks. She has developed a number of ground-breaking quantitative analysis models to serve a variety of objectives, including the identification of trends and associations through innovative historical and predictive modelling.
Rashmi advises a range of public and private sector organisations. She is widely known for creating innovative and robust data analytics solutions that provide tangible actionable intelligence to influence, inform and develop key policies and strategic objectives. Examples of her application areas include Risk Management, NHS analytics, Big Data, developing fraud identification and prediction frameworks for the UK Government and investment banks and calculating Return on Investment for Information Security spend within the Financial Services industry.
She is qualified to supervise Doctoral level research and has presented at numerous national and international conferences. In addition, she frequently contributes to a range of thought leadership and roundtable forums, and has a track record of publications.