Indrashekhar Mayengbam

I am a practical, solution-oriented Data Scientist with over two decades of experience designing robust, user-friendly, and efficient software solutions across diverse industries. My strong foundation in programming and problem-solving, coupled with a deep passion for data, empowers me to deliver impactful insights and solutions.

Earning a Data Science certification from the University of Cambridge has honed my expertise in advanced statistical methods, machine learning algorithms, and data visualization techniques. I have hands-on experience in key areas such as supervised and unsupervised learning, neural networks, deep learning, natural language processing, and time series analysis. My unique combination of programming proficiency and data science expertise equips me to analyse and interpret complex data effectively, empowering organizations to make confident, data-driven decisions.

University of Cambridge, UK – Data Scientist

March 2024 – November 2024

• Analyzing Quarterly Earnings Call - Bank of England

My team and I developed an AI platform designed to identify early signs of bank distress, aiming to prevent financial instability in the UK using Quarterly Earnings Calls as a data source. This was achieved by applying NLP techniques such as topic modelling, sentiment analysis, LangChain and RAG-based summarisation.

1. Created a scalable and efficient data preprocessing of unstructured data from PDF files.
2. Developed a proprietary algorithm to summarize the overall sentiment of a financial quarter that closely aligned with the stock market sentiment.

• Time Series Analysis for Sales and Demand Forecasting

Analysed historical book sales data to make data-driven decisions about their future investment in new publications.

1. ARIMA, SARIMA, SARIMAX
2. Time Series Forecasting with machine learning and deep learning
3. Gradient boosting models – Light GBM, XGBoost
4. RNN, LSTM, GRU, CNN and Hybrid Techniques 

• Topic Modelling customer feedback

Analysed customer feedback to understand what motivates members to join and what factors influence their behaviours once they have joined. 

1. Sentiment Analysis / Topic Modelling / Text Summarization
2. LLM tiiuae/falcon-7b-instruct - Huggingface
3. BERTopic
4. LDAmodel from Gensim

• Applying Supervised Learning to predict student dropout

Examined student data to predict whether a student will drop out and help institution's financial stability and students’ academic success and personal development. A high dropout rate can lead to significant revenue loss, diminished institutional reputation, and lower overall student satisfaction. 

1. XGBoost
2. Neural Networks - tensorflow, keras

• Customer Segmentation with clustering - Unsupervised Learning

Developed a robust customer segmentation to assist the e-commerce company in understanding and serving its customers better. This helped create a more customer-centric focus, improving marketing efficiency.

1. Hierarchical clustering 
2. k-means clustering

• Detecting the anomalous activity of a ship’s engine

Developed a robust anomaly detection system to protect a company’s shipping fleet by evaluating engine functionality. This helped develop fleet maintenance schedule and reduce fleet down time.

1. Interquartile Range (IQR)
2. One-class SVM
3. Isolation Forest 

• Bayesian thinking to a real-world business issue

Performed Bayesian parameter estimation and hypothesis testing to optimize the performance of an e-commerce platform.

1. A/B Test using PyMC

1. Bachelor of Engineering in Computer Science from Bharathiar University. Passed in the year 2000 with distinction.
2. Databricks certified Data Analyst Associate
3. Level 7 professional qualification (Career Accelerator Certification) in Data Science from University of Cambridge – 2024