• MS in Data Science - University of Texas, Austin, Sep 2022 - current

• PhD in Physics -  University of Minnesota, Twin Cities, June 2021

• MicroMasters in Data Science  -  University of California, San Diego, Aug 2019

• MS in Physics  -  University of Minnesota, Twin Cities, 2015

• Integrated MSc in Physical Sciences -  Visva-Bharati University, India, 2014

Research

 7+ years experience in:

• • Experimental design and hypothesis testing

  • Collecting qualitative and quantitative data

  • Data interpretation and analysis using statistical techniques

  • Using data visualization to communicate results to a wide audience

  • Designing new projects, handling various technology, and rapidly learning new skills

Machine Learning

• • Ability to creatively apply a wide range of ML techniques to solve diverse problems, such as classification, regression and recommendation systems

  • Hands-on experience with deep learning (using TensorFlow, Keras on IBM Cloud, Microsoft Azure, or Google Colab)

  • Experience building algorithms from scratch and also implementing state-of-the-art algorithms as described in literature

  • Firsthand experience with deploying ML models at enterprise level, using Flask, Docker and Kubernetes. 

  • POC and small-scale lightweight deployments with Streamlit, CherryPy, etc.

Software and Programming Languages

• • Proficient in Python, SQL, Interactive Data Language (IDL), Tableau, Jupyter notebooks

  • Experience using Mathematica, R, Matlab, LaTeX, SQL, MS Access, Apache Spark

  • Expertise in hardware descriptive languages and integration software: Verilog, Vivado, LabVIEW

Communication

• • Great technical writing skills and ability to communicate with people from various backgrounds

  • Experienced presenter at various conferences and meetings

  • Proven leadership, team-building, and interpersonal skills

Staff Data Scientist, Walmart Global Tech - June 2023 to current

• Led a team of 7+ members for AX (Associate Experience) Score project, a golden signal that indicates the health of workforce by combining ML based retention models, and productivity of all 1,300,000+ US Store associates. A causal framework was also established to provide insights into how various factors affect the outcome along with the chain of flow and spillover effects. Patent discussions in process. 

• Intricate understanding of workgroup roles and responsibilities for Stocking, Front End, Digital, etc., while developing a quantitative assessment of productivity scores, along a novel and unique standardization way. 

• Built GenAI tool and deployed a streamlit app on a GCP VM for leadership to explore and understand AX data. 

• Developed a truck traffic prediction ML model that utilizes a plethora of signals to predict timely arrivals. 

• Designed project roadmaps, proper code repo protocols, project environments, documentation processes, etc. 

• Helped with a Call-ins ML model for associates, where predictions were made on how many associates would call in at the last minute and be unavailable for work. This helps establish proper scheduling of the workforce with proper planning and minimal disruptions.

Staff Data Scientist, Seagate Technology - Oct 2022 to May 2023 

• Led the team in developing and implementing image analytics accelerators on Seagate Lyve Cloud

• Develop MLOps framework within the context of Lyve Cloud Analytics, to allow for faster turnaround times of both development and deployment of machine learning algorithms at large scale

• Assist customers with onboarding and successful deployment of their ML programs on Seagate Lyve Cloud Analytics platform

Senior Data Scientist , Seagate Technology - Jul 2021 to Oct 2022

• Led business requirement discussions with stakeholders across various teams and countries to identify the scope of machine learning algorithms in their production environment 

• Handled multiple machine learning projects concurrently, each with different requirements and level of scope: a hardware production line monitoring system, a rogue hardware detection program and an unsupervised anomaly detection program 

• Evaluated various analytical tools and MLOps platforms that help streamline the data science process, such as cnrvg.io and iguazio, for company-wide adoption 

• Led several python and machine learning training sessions for Citizen Data Science program – an initiative to teach data analytics skills to all people across Seagate 

• The hardware production line monitoring system involved machine learning algorithm that looked at time series data, queried and fetched from a Hadoop database using SQL, analyzed using methods like DBSCAN and dynamic time warping, and was deployed using services like Airflow, RedisQ, and Rancher to simultaneously monitor 1000+ machines and provide real-time notifications to stakeholders 

• The rogue hardware detection program aims to identify malicious components introduced onto parts sourced from external vendors, by comparing the x-ray images of obtained parts to the master design, utilizing modules like OpenCV and has been deployed for immediate testing using CherryPy 

• Optimized unsupervised anomaly detection program, that I previously built as an intern, to properly identify contamination defects in images of a certain hard-disk part, using reconstruction error from an autoencoder network 

Research Associate, University of Minnesota - 2015 to 2021

• • Currently developing new techniques to identify axial location of proteins with nanometer accuracy

  • Planned and setup new experimental designs for my research

  • Designed, developed and implemented Python, IDL programs to control FPGAs and perform laser scanning with user-defined scan paths, collect data and perform data analysis such as correlation analysis, and model fitting

Teaching Assistant, University of Minnesota - 2014 to 2017

• • Led lectures, discussion hours and laboratories for hundreds of students

  • Held labs, office hours and graded lab reports for upper level undergraduate courses that dealt with various analog circuits, LabVIEW and hardware descriptive languages like Verilog for 3 years

  • Exceptionally good at handling groups and delivering results, corroborated by the Outstanding TA award

Data Science and Machine Learning Intern, Seagate Technology - Jan 2020 - Jan 2021

• Participated in initiation meetings with stakeholders to identify the business requirements 

• Explored various machine learning research papers related to unsupervised anomaly detection, to identify proper solution for the business requirement 

• Incorporated one of the research papers, with appropriate modifications to identify anomalies in images 

• Developed a dual step algorithm containing convolutional neural networks followed by clustering using gaussian mixture model to identify anomalies, and deployed the solution using tools like Docker, RabbitMQ and CherryPy 

• Optimized various other existing machine learning programs at Seagate using techniques like distributed training (horovod) and Nvidia AMP 

Research Intern, Integrated Science Education and Research Center - June 2012 to May 2013

• • Performed spectroscopic analysis of various composites and studied its industrial applications

  • Published the results in a peer reviewed journal

Research Intern, Tata Institute of Fundamental Research - June to July 2012

• • Assisted in the construction of a linear accelerator and beam splitter

  • Optimized beam current of Electron Cyclotron Resonance Ion Source

Research Intern, Tata Institute of Fundamental Research - May to July 2011

• • Conducted experiments to estimate age and source strength of samples like Am241 and Co57

  • Characterized multiple detectors for their efficiency and resolution

Peer reviewed publications

1. Differentiating Luminal and Membrane-Associated Nuclear Envelope Proteins, Biophysical Journal, 2020 - Link

2. Sensitive Detection of Protein Binding to the Plasma Membrane with Dual-Color Z-Scan Fluorescence, Biophysical Journal, 2020 - Link

3. Time-shifted mean-segmented Q data of a luminal protein measured at the nuclear envelope by fluorescence fluctuation microscopy, Data in Brief, 2020 - Link

4. Identifying hetero-protein complexes in the nuclear envelope, Biophysical Journal, 2019 - Link

5. Protein oligomerization and mobility within the nuclear envelope evaluated by the time-shifted mean-segmented Q factor, Methods, 2019, Volume 157 - Link

6. Optical studies of poly(9,9-di-(2-ethylhexyl)-9H-fluorene-2,7-vinylene) and its nanocomposites, Journal of Applied Spectroscopy, 2015, Volume 82, Issue 5 - Link

Awards

• Outstanding Teaching Assistant - University of Minnesota, Twin Cities, May 2016

• INSPIRE scholarship - Department of Science and Technology, India, 2009 to 2014

• Merit awards for being in the top 3 at District level science fairs - Karimnagar, India, 2003 and 2005

Invited talks

• "Exclusion of RNA-Associated Proteins from the Cell Cortex Observed by Dual Color Z-Scan Fluorescence Microscopy", Biophysical Annual Meeting, San Diego, CA, Feb 15 to 19, 2020

Poster presentations

• "Assessing Location and Distribution of Proteins at the Nuclear Envelope from Dual Color Z-Scan Intensity Profiles", Biophysical Annual Meeting, Feb 22 to 26, 2021

• "Exclusion of RNA-Associated Proteins from the Cell Cortex Observed by Dual Color Z-Scan Fluorescence Microscopy", Biophysical Annual Meeting, San Diego, CA, Feb 15 to 19, 2020

• “Achieving Axial Super-Resolution with the Two-Photon Dual-Color Z-Scan Method”, Biophysical Annual Meeting, Baltimore, MD, Mar 2 to 6, 2019

• “Identifying the Axial Location of Proteins at the Nuclear Envelope with Nanometer Resolution”, Biophysical Annual Meeting, San Francisco, CA, Feb 17 to 21, 2018

Certifications

• TensorFlow Developer, Google - Link

• Deep Learning Specialization, deeplearning.ai - Link

• Advanced Data Science Specialist, IBM - Link

• Deploying Scalable Machine Learning for Data Science, LinkedIn

• Tableau Desktop Specialist, Tableau - Link, Badge

• Big Data Analytics Using Spark, UC SanDiego - Link

• Machine Learning Fundamentals, UC SanDiego - Link

• Probability and Statistics in Data Science using Python, UC SanDiego - Link

• Python for Data Science, UC SanDiego - Link

• President, Board of Directors, Como Student Community Cooperative, Minneapolis, Jul 2019 to Jun 2020

• Member, Board of Directors, Como Student Community Cooperative, Minneapolis, Sep 2018 to Jun 2020

• Voting representative for School of Physics at the Council of Graduate Students, University of Minnesota, Twin Cities, 2016 to 2017

• Team manager, District Netball Team, Karimnagar, India, 2006

• Recipient of gold medal in a National level painting competition, India, 2004

• Volunteered for organizations like Habitat for Humanity International