TImeline:

๐Ÿ“† Feb 2023 - now

Location:

๐Ÿ“ย San Francisco, CA ๐ŸŒ‰

Postion(s)/Work:

๐Ÿง‘โ€๐Ÿ’ปย Applied Scientist ๐Ÿ‘จโ€๐Ÿซ

Description of work:

As an applied Scientist my primary work is two folded:

Firstly, I collaborate with other researchers on building machine learning models meant to improve Ripple's solutions for facing digital asset markets. This requires me to apply my ML modeling and statistical analysis skills to translate business problems into machine learning problems in order to develop optimal solutions.

Secondly, I contribute to tooling that allows for the above models to be built/experimented on at scale using our own Experimentation Bench. Once a good enough model is built, I also contribute to making sure the models are seamlessly deployed into production. This allows me to tackle real-world issues such as missing or inconsistent data, its latency, and data cleanness efforts. Continuous deployment and experimentation keeps me close to the source of truth, its speculations and its results.

Apart from the above, I also lead and collaborate on the AI (read LLM) application efforts in the company on varied use cases. Our latest effort is on LLM fine tuning (or its need thereof) to help builders with improving code coverage across our proprietary codebase in our own style of codebase maintenance.