Anoop Hallur
Data AnalystStaff Engineer
Salesforce
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Staff Engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Carnegie Mellon University
M.S, Electrical & Computer Engineering
January 1, 2014 – January 1, 2015
B. M. S. College of Engineering
Bachelor of Engineering (B.E.), Electronics and Communications Engineering
January 1, 2008 – January 1, 2012
Salesforce
Lead Member of Technical Staff
April 1, 2020 – April 1, 2026
Hybrid
Splunk
Software Engineer
October 1, 2018 – April 1, 2020
San Fransisco
Solvvy
Machine Learning Engineer
April 1, 2017 – September 1, 2018
San Francisco Bay Area
Adobe
Imaging Services Developer
May 1, 2015 – April 1, 2017
San Francisco Bay Area
ThoughtWorks
Application Developer
August 1, 2012 – December 1, 2013
Bangalore, India
Cloud Based Analytics Service
November 1, 2014 – Present
~> Built a cloud based analytics service analyzing twitter data dump of 1TB data ~> Built a REST API to serve for customized queries from over 1TB of data collected from twitter.com. Implemented the ETL logic using Hadoop on Amazon EC2 clusters and built the front end RESTful API using SPRING MVC framework to serve about 10000rps on average. Used HBASE as high performance key value store.
Parallel Clustering using Open-MPI
October 1, 2014 – Present
Implemented parallel k-means algorithm to perform clustering on DNA sequences and 2-D data points in a distributed computing environment using Open-MPI and calculated the speedup compared to the sequential implementation of the k-means Algorithm.
Applying Deep Learning Methods to Keyword extraction
September 1, 2014 – Present
~> Applied Deep Learning Techniques for keyword extraction from text samples ~> Used 20 GB data from stackexchange.com as sample data set ~> Implemented several other techniques for keyword extraction as well for baseline results, used stanford parser and lancaster stemmer from NLTK for quicker development ~> Improved precision and recall rates by 20% from the baseline results
Custom Speech Recognizer for Digits
January 1, 2014 – Present
~> Built a speech recognizer from scratch for a limited vocabulary consisting of digits 0-9 ~> Able to handle continuous input stream and perform end pointing at 7 and 10 digit phone numbers ~> Single digit accuracy was over 80% and sentence accuracy was ~45%
Text region detection in an image
January 1, 2014 – Present
~> Built a MATLAB based tool for detecting presence of text in an image. ~> Extracted features from stroke width transform of an image, feed it into SVM for classification ~> Achieved an accuracy of about ~60%
Acoustic Echo Cancellation Calibration suite
January 1, 2012 – Present
~> Built an evaluation and calibration unit for Acoustic Echo Cancellation(AEC) unit housed in an android phone(OMAP 4 device) ~> Used network libraries for simulating AEC performance ~> Built the user interface and the accompanying digital filters for fine tuning the performance of AEC based on the user input
Cultural Fit Analysis
The candidate has a diverse background spanning multiple companies (Salesforce, Splunk, Solvvy, Adobe, ThoughtWorks) and a variety of technical projects, including personal initiatives. This breadth of experience suggests adaptability and a willingness to tackle different challenges. The focus on data, machine learning, and large-scale systems aligns well with a data-driven culture. However, the target role of 'Data Analyst' might be a slight mismatch given the candidate's 'Lead Member of Technical Staff' and 'Machine Learning Engineer' experience, which often involves more advanced engineering and less pure analysis.
Soft Skills & Operational Fit
The candidate's project descriptions and work history indicate strong problem-solving abilities, a proactive approach to system improvements, and experience in leading technical initiatives. The transition from individual contributor to lead roles suggests leadership potential and operational awareness. However, without specific soft skill assessments, this remains an inference.