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Machine Learning at FactSet
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I have a Masters degree in Computer Science from Wright State University. My thesis topic was location prediction of Twitter users based on the contents of their tweets using Wikipedia as a knowledgebase. I am interested in leveraging machine learning to develop solutions for extraction of structured data from unstructured text and predictive analytics.
Wright State University
Master's degree, Computer Science
January 1, 2012 – January 1, 2014
Savitribai Phule Pune University
Bachelor of Engineering (BE), Computer Science
January 1, 2003 – January 1, 2007
FactSet
Principal ML Engineer
October 1, 2022 – Present
FactSet
Lead Machine Learning Engineer
September 1, 2020 – Present
FactSet
Senior Machine Learning Engineer
January 1, 2018 – Present
FactSet
Software Engineer
March 1, 2015 – Present
ezDI, LLC
Summer Intern
May 1, 2013 – July 1, 2013
Gujrat, India
Kno.e.sis - Ohio Center of Excellence in Knowledge-enabled Computing
Graduate Research Assistant
August 1, 2012 – December 1, 2014
Fairborn, Ohio
Accenture
Senior Software Engineer
September 1, 2010 – August 1, 2012
Accenture
Software Engineer
January 1, 2009 – October 1, 2010
Accenture
Associate Software Engineer
July 1, 2007 – September 1, 2008
Bengaluru, Karnataka, India
Location Prediction of Twitter Users Leveraging Wikipedia
January 1, 2014 – Present
The geographic location of a Twitter user can be used in many applications such as Personalization and Recommendation systems. This work explores the use of an external knowledge-base (Wikipedia) to predict the location of a Twitter user based on the contents of their tweets and compares this approach to the existing statistical approaches. The key contribution of this work is that it does not require a training data set of geo-tagged tweets as used by the state-of-the-art approaches.
Traffic Analytics using Textual and Sensor Data
January 1, 2013 – Present
Traffic congestions have become a major issue in many cities around the world. At Kno.e.sis, researches work on understanding city issues such as traffic problems to provide insights to decision/policy makers. We pursue this understanding utilizing a unique approach of processing both machine sensor data and citizen sensor data related to traffic. Citizen sensor observations complement or corroborate machine sensor observations and when processed together leads to deeper insights into a Cyber-Physical-Social system like a city.
PREDOSE
August 1, 2012 – Present
The overall aim of PREDOSE is to develop techniques to facilitate prescription drug abuse epidemiology, related to the illicit use of pharmaceutical opioids. As a part of the bigger project, we worked on extracting target specific sentiments from web forum posts using natural language processing and machine learning algorithms. The goal was to identify trending positive or negative sentiments associated with specific drugs with research interest.
Cultural Fit Analysis
The candidate has a strong background in Machine Learning and Data Science, which aligns with roles requiring analytical rigor. The progression from Software Engineer to Principal ML Engineer at FactSet shows long-term commitment and growth. However, the projects listed are heavily focused on academic/research-oriented ML/NLP tasks, and the resume lacks specific backend engineering technologies (e.g., specific programming languages, frameworks, databases, cloud platforms) that are typically expected for a 'Backend Engineer' role. This might indicate a potential mismatch if the target role is purely backend development without a significant ML component.
Soft Skills & Operational Fit
The candidate's resume highlights roles involving research and application of complex algorithms, suggesting strong analytical and problem-solving skills. The progression through various roles at FactSet indicates dedication and growth within an organization. However, without psychometric test results or interview data, it is difficult to assess stress handling, team collaboration, or work attitude.