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Responsible Technology and AI Governance SME and Educator
I am an electrical engineer who identified her true passion for data science, AI, technology ethics and governance. - World Economic Forum Professional Fellow - Open Source Contributor: Model Asset eXchange, Data Asset eXchange, TensorFlow, and AI Fairness 360 - Tech Ethics Advocate: Gave over 75 talks over the past 2 years (2018 - Present) - Data Science Mentor and Instructor Happy to discuss more. Medium- https://medium.com/@saishruthi.tn Github- https://github.com/SSaishruthi Views are my own and do not represent my employer.
San José State University
Master’s Degree, Electrical Engineering
January 1, 2016 – June 1, 2018
Sri Sairam Engineering College
Bachelor of Engineering (B.E.), Electrical and Electronics Engineering
January 1, 2010 – January 1, 2014
IBM
Senior Responsible Tech and Governance Program Manager
April 1, 2025 – Present
World Economic Forum
Professional Fellow
August 1, 2023 – March 1, 2024
San Francisco Bay Area
IBM
Ethics by Design / Board Program Advisor and Data Scientist
June 1, 2022 – August 1, 2025
IBM
Advisory Data Scientist - AI Strategy & Innovation
July 1, 2021 – June 1, 2022
IBM
Technical Lead - CODAIT Machine Learning
February 1, 2021 – July 1, 2021
IBM
IBM Developer Advocate / Data Scientist
July 1, 2018 – July 1, 2021
Method Data Science
Community Data Scientist
June 1, 2018 – September 1, 2018
San Jose State University
Research Assistant (Data Mining and Deep learning)
December 1, 2017 – May 1, 2018
San Jose State University
Teaching Assistant
September 1, 2017 – December 1, 2017
MOBODEXTER
Software Engineer
June 1, 2017 – December 1, 2017
WA
San Jose State University
Research Assistant
March 1, 2017 – May 1, 2017
California
San Jose State University
Instructional Student Assistant
March 1, 2017 – May 1, 2017
California
Tata Consultancy Services
System Engineer
July 1, 2014 – July 1, 2016
Chennai Area, India
Poverty Prediction – Competition conducted by Driven Data (Language: Python)
February 1, 2018 – Present
- Designed a model to predict poverty using pre-processing, sampling, boosting, pipelining and tuning techniques. - 7 models have experimented and XGBOOST with parameter tuning outperformed others. - Obtained mean log loss of 0.18191 and ranked in top 10% (Total participants 2310 - Rank 201).
Amazon Catalog product classification (Language: Python)
November 1, 2017 – Present
Amazon UCSD product metadata is used. 1000 samples from 20 categories were taken for classification purpose. Implemented K-nearest neighbour algorithm to obtain the top three best catalog paths for placing new products. Experimented different Naïve Bayes algorithm combinations obtain best classification of reviews to products Inferred sentiment from reviews and designed review summarizer using keras.
Drug Activity Prediction model (Language: Python)
November 1, 2017 – Present
- Experimented different dimensionality reduction techniques such PCA, SVD, LDA and Random Projections to select features that have impact on prediction. - Developed training methods to train highly imbalanced data and implemented cross-validation techniques to check performance of the model - Experimented different algorithms such as Adaboost, Neural network, Ensemble classifier, Naive Bayes, Decision tree, Gradient Boosting classfier, SVM, Extra tree and nearest neighbors. - Achieved accuracy and F1 score of 83.5%
Performance Analysis of machine learning algorithms on hand written digit dataset (Language:Python)
November 1, 2017 – Present
- Analyzed performance of different machine learning algorithms such as Linear Classifier, KNN, Naive Bayes, Random Forest, Decision Tree, Logistic Regression, Radial basis neural network and one/two hidden layer neural network on the benchmark dataset of MNIST for recognizing hand-written digits. - Performed dimensionality reduction on dataset using PCA, SVD and LDA techniques leading to improved performance. - Experimented efficiency and performance of different neural network activation functions (sigmoid, ReLU and tanh) and regularization techniques (Lasso and ridge).
CDiscount Image Classification
November 1, 2017 – December 1, 2017
- Model is built to classify products of CDiscount, an e-commerce company, based on their images. - 4 layer CNN, VGG16, VGG19, ResNet50, Inception are the algorithms used for classification - Handled unbalance in data using SMOTE over-sampling and data augmentation. - Libraries such as tensorflow and keras were used with Python as programming language
Sentiment Analysis of IMDB movie reviews(NLP)
October 1, 2017 – Present
- Design and engineer features from IMDB review data. Techniques such as vectorizer and cosine similarity were used to extract feature details from the review data. - Handled HTML artifacts using techniques like parsing and experimented stemming to preprocess data. - Implemented nearest neighbor classification algorithm to infer sentiments and obtained accuracy of 85.8% - Recognized as one of the top three classifying algorithms among 40 other similar algorithms.
Medical Application using WebRTC
April 1, 2017 – May 1, 2017
- Real time communication was established between peers where audio, video and data are exchanged - Used API’s such mediastream, RTCpeerconnection and RTCdatachannel for establishing communication. - STUN and TURN servers are used to enable p2p communication - Developed medical app using webRTC where patients can communicate to doctors and vice versa
Wireless network Hacking and Penetration Testing
March 1, 2017 – May 1, 2017
-Setting up own wireless network for carrying out penetration testing and gathering AP information -Cracking WPA/WPA2 encryption using dictionary, bruteforce, rainbow table, evil twin and hashcat attack -Carrying MITM on the setup network and get full access of device by creating backdoor -Protection methods are carried out to keep network safe from attacks
Implementation of MPLS, Advanced MPLS and Traffic Engineering
March 1, 2017 – April 1, 2017
- Designed and configured MPLS network using GNS3. - Studied and used CISCO routers C3640 and C700 for creating MPLS network. - Simulated advanced MPLS using ISIS as routing protocol, VRF and BGP. - Established and monitored communication between Provider -Provider edge, Provider edge - Provider edge, customer - provider. - Implemented traffic engineering in the created advanced MPLS network and observed the performance.
SIP Implementation
March 1, 2017 – April 1, 2017
- Using asteriskwin32 as server and X-lite softphone as client, various functionalities of call are experimented - Created basic SIP client using python and PJSIP library - Communication is established between the created SIP client and X-Lite client - Activities are captured using wireshark packet analyzer and call flow is observed and recorded
IP Packet analysis and correlation of IP address to locations
February 1, 2017 – April 1, 2017
- IP Packets are created and analysed using scapy and dpkt . Python is used as scripting language. - Correlating IP address of the packet with the location using geolocation database like GeoCityLite - Integrating and displaying IP packet location in Google Map
Weather Forecasting App
October 1, 2016 – Present
- Retrieved weather forecast from openweathermap API - JSON used for parsing weather information and HTTP protocol for invoking Android Openweathermap - Layout is designed and Asynctask will run network task in main thread to prevent ANR Issues
Protection and Control measures in fuel stations
February 1, 2014 – April 1, 2014
- Surveyed 50 fuel stations to analyse hidden and unnoticed problems faced day-to-day - Resolved the problem of fuel density change due to temperature instability which caused heavy losses - Remodelled adulteration and level monitoring system which reduces manual work by 80 percent - Temperature control setup, sensor, GSM and Embedded C were used
Building Agentic AI Applications with a Problem-First Approach
Maven
June 24, 2026 – Present
Data Scientist Project Badge - Level 1
IBM
June 24, 2026 – Present
IBM Mentor
IBM
June 24, 2026 – Present
Open Source Strategic Committer
IBM
June 24, 2026 – Present
Data Science Orientation
Coursera
June 24, 2026 – Present
IBM Recognized Speaker/ Presenter
IBM
June 24, 2026 – Present
DATA SCIENCE WORK TRAINING PROGRAM
Method Data Science
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Introduction to R Course
DataCamp
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
The Complete SQL Bootcamp
Udemy
June 24, 2026 – Present
Introduction to Computer Vision | OpenCV in python
Udemy
June 24, 2026 – Present
OCJP - Oracle Certified Java Professional, Java SE Programmer
Oracle
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio is highly diverse, showcasing interests in machine learning, networking, embedded systems, and even cybersecurity. Their professional experience at IBM and the World Economic Forum, particularly in AI ethics and governance, indicates a strong alignment with responsible technology development and a collaborative mindset. The breadth of skills and project types suggests an individual who is eager to learn and apply themselves to various challenges. However, the recent career trajectory towards AI governance might indicate a shift away from a purely technical data analyst role, which could impact cultural fit if the target role is strictly focused on traditional data analysis tasks.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and experimental approach to problem-solving, trying multiple models and techniques to achieve optimal results. Their experience as a Technical Lead and Developer Advocate at IBM suggests strong communication and leadership potential. The diverse range of projects, from machine learning to networking and embedded systems, demonstrates adaptability and a broad technical curiosity. However, the resume does not provide direct evidence of operational fit for a pure Data Analyst role, as much of the recent experience leans towards AI strategy and governance rather than hands-on data analysis or dashboarding.