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Staff MLE at Warner Bros Discovery (HBO Max)
I have over a decade of professional expertise in being both a Data Scientist and ML Engineer. i.e., I can prototype ML solutions using state of the art algorithms, and also integrate them in production systems using established software engineering practices. I enjoy learning the fundamentals behind ML algorithms so they can be better applied in practice; and the technologies to implement scalable algorithms. My expertise is in * ML algorithms & feature engineering * Data Platforms + pipelines (both streaming and batch) * Distributed Databases and Datalakes (both online and offline) * Java microservices (maven, springboot etc) What motivates me is working in a collaborative team where we can learn from one another, and growing together in our respective areas. PS: Apologies if I don't get to promptly respond to messages re: new openings
UC Irvine
MS, Computer Science
N/A – Present
M.S.Ramaiah Institute of Technology
BE, Computer Science
N/A – Present
Warner Bros. Discovery
Machine Learning Engineer
December 1, 2020 – Present
Bellevue, Washington, United States
Condé Nast
Machine Learning Engineer
January 1, 2016 – January 1, 2020
Digital Reasoning
Data Scientist
January 1, 2012 – January 1, 2016
Cultural Fit Analysis
The candidate has a strong background in Machine Learning Engineering and Data Science across diverse industries (media, entertainment, financial intelligence). While the target role is 'Data Analyst', the candidate's experience is heavily skewed towards advanced ML model development and engineering, which might indicate an overqualification or a potential mismatch in day-to-day responsibilities for a typical Data Analyst role. The breadth of skills in ML and data processing is high, but direct alignment with core data analysis tasks (e.g., dashboarding, reporting, business intelligence) is less explicit. This could lead to a cultural fit challenge if the role is purely analytical and less focused on ML model development.
Soft Skills & Operational Fit
The candidate's resume highlights significant experience in complex problem-solving and system architecture, suggesting strong analytical and operational capabilities. The descriptions of implementing and optimizing various ML algorithms imply a proactive and results-oriented approach. However, without specific assessment data, soft skills like teamwork, leadership, and communication cannot be definitively assessed.