Prakruthi Prabhakar
Data AnalystMachine Learning at LinkedIn
LinkedIn
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Machine Learning at LinkedIn
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Product. Engineering. Machine Learning. Natural Language Processing. Software Development. Data Science.
Carnegie Mellon University
Master of Science in Computer Science, Computer Science
January 1, 2016 – January 1, 2017
Indian Institute of Technology, Madras
BTech and MTech (dual degree), Electrical Engineering
January 1, 2009 – January 1, 2014
Senior Staff Software Engineer, Machine Learning
September 1, 2024 – Present
Staff Software Engineer, Machine Learning
March 1, 2022 – August 1, 2024
Senior Software Engineer, Machine Learning
March 1, 2020 – February 1, 2022
Machine Learning Engineer
April 1, 2018 – March 1, 2020
Machine Learning and Relevance Engineer Intern
May 1, 2017 – August 1, 2017
San Francisco Bay Area
Carnegie Mellon University
Graduate Teaching Assistant
January 1, 2017 – May 1, 2017
Greater Pittsburgh Area
Big Data Labs, American Express
Research Engineer
July 1, 2014 – July 1, 2016
Bangalore, India
American Express
Summer Internship
May 1, 2013 – July 1, 2013
Big Data Labs, Bangalore
Qualcomm
Summer Internship
May 1, 2012 – July 1, 2012
Hyderabad Area, India
Texas Instruments
Summer Internship
May 1, 2011 – July 1, 2011
Bengaluru Area, India
Show, Demand and Tell - Goal Directed Image Captioning
January 1, 2017 – May 1, 2017
Worked on Image captioning problem given an image and a target demand word. Explored attention models for combining visual and language modality.
Parallelizing Pretraining of Deep Neural Networks using Stacked Autoencoders
January 1, 2017 – May 1, 2017
Implemented and analyzed data parallel and model parallel approaches to pretraining a deep neural network using stacked autoencoders. Developed generic and optimized multi-GPU implementations of pretraining for both data parallel and model parallel approaches in Tensorflow. For more information, please visit https://parapret.github.io/ppdnn/
Dense Subtensor Mining
January 1, 2017 – May 1, 2017
Implemented D-cube algorithm to detect top-k dense blocks in tensors. Analyzed the network and fraudulent attacks on real-world datasets like DARPA network connections, Amazon and Yelp product review datasets through the dense blocks detected.
Face Swapping
September 1, 2016 – December 1, 2016
Implemented face swap using face detection and homography
Prediction of Next word given speech signal and associated emotions
September 1, 2016 – December 1, 2016
Joint modeling of speech signal and associated emotions to predict next spoken word using an LSTM architecture
Dual Degree Project: Modeling of glitching effects in estimation of Dynamic Power consumption
July 1, 2013 – June 1, 2014
• Identified patterns in glitch power consumption of circuits with process variations through Monte Carlo simulations • Implemented an ambiguity interval based algorithm to estimate bounds on power consumption • Implemented a probabilistic model to identify high glitch nodes in a circuit • Devised an approach to estimate power saving by blocking glitch propagation at a node and validated the approach by introducing latches at those nodes
Centralised inverter for Variable Frequency Drive
November 1, 2012 – December 1, 2012
- Modeled a technique to install Variable Frequency Drive efficiently for house-hold motor-based appliances using centralised inverter - Presented poster demonstrating reduction in AC-DC conversion losses
Smart Switchboard
November 1, 2012 – December 1, 2012
• Developed a low-cost versatile smart switchboard for curbing power losses in household appliances due to human negligence at 50% of the conventional cost • Implemented automatic switch-off and power monitoring features with a timer and LCD display
Social Network Analysis
Coursera Course Certificates
June 24, 2026 – Present
R Programming
Coursera Course Certificates
June 24, 2026 – Present
Developing Data Products
Coursera
June 24, 2026 – Present
Mining Massive Datasets
Coursera
June 24, 2026 – Present
Statistical Inference
Coursera
June 24, 2026 – Present
Reproducible Research
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career trajectory is heavily focused on Machine Learning and Software Engineering, with a strong emphasis on research and development. While there are transferable analytical skills, the target role of 'Data Analyst' might be a step down in terms of technical depth and scope compared to their 'Senior Staff Software Engineer, Machine Learning' experience. The projects are diverse in ML/AI but less focused on traditional data analysis, reporting, or business intelligence. This could indicate a potential mismatch if the Data Analyst role is not heavily focused on advanced ML/statistical modeling.
Soft Skills & Operational Fit
The candidate's extensive project history and progression through senior roles at LinkedIn suggest strong problem-solving, analytical thinking, and leadership skills. The role as a Graduate Teaching Assistant also indicates communication and mentorship abilities. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.