Ishaan Jolly
An Optimisation
and Machine Learning
guy juggling life, work and passion!
I am a Data Scientist at NatWest Group, where I began my journey as a Consulting Data Scientist within Retail Analytics and Decisioning. My role soon evolved into a permanent position within Financial Analytics and Modelling, allowing me to delve deeper into complex and impactful projects.
Throughout my career, I have gained invaluable experience not only through the projects I’ve been involved in but also from the people around me. I have been fortunate to work with individuals who entrusted me with project ownership, even when I was relatively new to the companies I’ve worked for. This trust has given me the freedom to explore, experiment, and find purpose in my work.
I am passionate about open-source and actively contribute to various projects. While my focus often lies in Machine Learning and Operational Research, I enjoy exploring and engaging with a diverse range of topics within the open-source community.
Originally from New Delhi, India, I now reside in Edinburgh, UK, where I earned my MSc in Operational Research from the University of Edinburgh.
Outside of my professional life, I’m commited towards environmental causes and enjoy dancing (Bhangra, Bollywood, Afrobeats, and Hip Hop), cycling, hiking, and (only sometimes) running. If you like anything you see, feel free to connect with me on LinkedIn or send me an email - I love to connect and chat with like-minded people!
Promoted to permanent employee from my consulting position.
I work with Financial Modelling and Analytics, so far I have
Designed an end-to-end multivariate forecasting model on AWS SageMaker to predict customer behaviours for savings profitability modelling across 22 on-sale products; selected for NatWest’s Data Science and Engineering Conference 2023; received a ‘B’ rating from the Model Risk Evaluation; commended for robust re-forecasting, coding practices, and meticulous testing
Developed a live customer-behaviour updates process for overdrafts profitability modelling; praised for a unified approach across retail, finance, pricing, and analytics to monitoring customer behaviour and journey.
Tech stack: Python
, AWS Sagemaker
, PySpark
, Git
, AWS Athena
NatWest Group (Client)
I worked within Retail Analytics and Decisioning team, where, amongst many other things, I
Developed three propensity to upgrade models targeting lower-paid account customers for upgrades; utilized decile-based response rates and rigorous risk governance; generated £100K+ in benefits; productionised the model for delivering monthly outputs to stakeholders.
Implemented a causal forecasting model integrating customer feedback, transactional data, and external factors; proactively addressed potential mis-sellings, saving £0.5 million; showcased at NatWest’s mini Data Science and Engineering Conference 2023.
Developed a strategic data resource using Teradata SQL, Snowflake, and Hive; identified and mitigated criminal behaviour in personal accounts used for business transactions.
Developed uplift models to identify customers most likely to upgrade from non-fee to higher-paid accounts given previous customer campaigns; resulted in a 30% increase in the packaged accounts customer base in 2023.
Employee of the month: December 2022, January 2022, and August 2023
Research and Development (Internal)
Led an intern project on generating synthetic data for financial transactions using conditional and generalised adversarial networks, and autoencoders.
Mentored two non-data colleagues and two junior data scientists in their Data Science and Python journey.
Developed a Hybrid LSTM-Random Forest model for forecasting electricity consumption at UK EV charging depots; optimised new charging point locations with a location-allocation model considering distance constraints and existing infrastructure.
Received 10+ Gold, Silver, and Bronze Employee Recognition Star Awards for contributions to projects, mentoring, and community engagement.
Tech stack : Python
, AWS Sagemaker
, PySpark
, Git
, Snowflake
, Bit bucket
, Teradata
, Databricks
,
inner source
Tech stack: Python