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Data Scientist @ Siemens
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Data Scientist currently focusing in Machine Learning/Deep Learning
Wayne State University
Master’s Degree, Electrical and Computer Engineering
August 1, 2015 – April 1, 2017
National Institute of Technology Silchar
Bachelor’s Degree, Electrical, Electronics and Communications Engineering
January 1, 2009 – January 1, 2013
Siemens
Data Scientist
October 1, 2025 – Present
Austin, Texas, United States · Hybrid
Altair
Data Scientist
October 1, 2017 – September 1, 2025
Austin, Texas, United States · Hybrid
Interactions LLC
Data Scientist
May 1, 2017 – September 1, 2017
Northville,Michigan
Wayne State University
Graduate Research Assistant
September 1, 2016 – April 1, 2017
Detroit Metropolitan Area
Deep language model for solving large anagrams:
February 1, 2016 – Present
Generating meaningful English sentences for a given constraint set of characters : Performance of Deep Language Model: Input:NNNaaaaaaabcccddddeeeeeeeeefffffgghhhiiiiiiiiijkllllllllmmnnnnnoooooooooooooppppprrrrrrrrrrsssssstttttttttuuuuvw<> Output: mr._<unk>_said_the_move_is_expected_to_restructure_decision_to_a_proposal_ and_said_the_move_in_the_rest_of_the_more_market_rose_N_to_N_N_million. Best Anagram-Solver available on internet: Output: Life_Assurance_Ltd_v_Greater_Johannesburg_Transitional_Metropolitan_Council • English PTB data set is tokenized and converted into integer ids. • Log perplexity error on training data is reduced from 770 to 7.8. • Achieved 96% accuracy in small sentences and 92% in long sentences. • Tokenized input and output texts are converted into dense vectors using Word2vec in Tensorflow. • Tensorflow Embedding seq2seq model is used to train the algorithm. • LSTM cell is used for the seq2seq model.
Photo Optical Character Recognition for Autonomous Vehicles
August 1, 2015 – February 1, 2016
• Designed an algorithm to read road signs for autonomous vehicles. • Achieved 99% accuracy on training data, 96% on testing data. • Bumped the accuracy to 98% on testing data using LSTM cell and data parallelism using CUDA toolkit which integrated 4 GPUs( Tesla K40). • The algorithm can perform text recognition, character segmentation and character recognition. • Text recognition is done with sliding window algorithm and trained with a binary classifier. • Character segmentation is trained with binary classifier. • Character recognition is trained with multi class classifier which has 36 classes,26 letters(A-Z)+(0-9) • Programming is done using Python with open source libraries Scipy and Numpy and Tensorflow is used as the building platform.
Embedded System and Sensor fusion: In vehicle control system using CAN protocol
May 1, 2012 – August 1, 2012
• Integrated CAN bus controller, ARM as main control module and LCD display to provide interface. • Developed and implemented a digital driving system to improve driver-vehicle interface. • Used CAN bus(1) to meet real time requirements such as engine speed, wheel speed, throttle pedal locations. • Used CAN bus(2) for locker, windows, mirrors, interior lights feedback controls. • ARM7TDMI-S a 32 bit microprocessor based on RISC principles is integrated with CAN buses and LCD. • In vehicle control system is programmed using Embedded C and debugged with MPLAB XIDE. • A temperature sensor LM 35 integrated with the main module . • A fan and an alarm are programmed to turn on by the main module if the temperature feedback from the sensor crosses 250 degree F.
Cultural Fit Analysis
The candidate's project portfolio shows a diverse range of technical interests, from deep learning for language models and computer vision to embedded systems. This breadth suggests adaptability and a continuous learning mindset. The experience as a Data Scientist aligns well with roles requiring analytical rigor and data-driven decision-making, which are common in many organizational cultures. The target role of 'Data Analyst' is a slight shift from 'Data Scientist', which might require an emphasis on business intelligence, reporting, and dashboarding skills not explicitly detailed in the resume.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and a results-oriented approach, evidenced by the focus on accuracy improvements and performance metrics. The detailed technical descriptions suggest good communication of complex technical concepts. However, without direct assessment data for soft skills, this remains an inference.