Nidhi Chakravarthy

Data Engineer | Python Developer | ML and AI Enthusiast

Nidhi
  • Hello! I am a passionate and results-driven Data Enthusiast based in London, with a strong foundation in Data Science and Machine Learning.
  • I hold a Master’s degree in Advanced Computer Science, where I specialized in Data Science, Machine Learning, and Artificial Intelligence.
  • My experience spans across data analysis, data visualization, predictive modeling, and solving real-world problems through data-driven insights.
  • With a keen interest in turning raw data into actionable insights, I continuously strive to improve my skills and stay updated with the latest trends in data science and ML.
  • My work is guided by a combination of technical expertise and a curiosity to explore new ways to drive value from data.
  • I am currently open to new opportunities where I can contribute my skills and grow as part of a dynamic team. Let's connect and explore how we can collaborate!

Skills

Python

Java

SQL

JavaScript

Machine Learning

Pandas

NumPy

Hadoop

Apache Spark

MySQL

Git

Linux/Unix

MATLAB

Experience

Data Analyst, London

Holley Holland Group, UK | October 2022 - Present

  • Implemented data quality rules and automated data workflows using Python, reducing report generation time by 40% and improving overall data accuracy.
  • Led comprehensive Quality Assurance on interactive dashboards for stakeholders, enabling real-time monitoring of 20+ data quality metrics and trends, which improved issue detection speed and reporting reliability.
  • Obtained certification as a Solidatus model builder, developing foundational skills in data lineage and metadata management.

    • Bank of New York Mellon

  • Generated and Quality Assured data lineage solutions, improving transparency, regulatory compliance, and operational efficiency in reporting guaranteeing reliability in alignment with business logic.
  • Authored technical documentation and delivered solution walkthroughs to stakeholders, facilitating project handover and user adoption, enabling seamless onboarding for new team members and reduced training time by 30%.
  • Collaborated with cross-functional teams to seamlessly integrate data lineage with existing processes, contributing to operational efficiency and risk mitigation by 15%.

    • NASDAQ

  • Co-ordinated vendor data optimization for the stock exchange index business, saving costs by 40% through Power Query and SQL-driven data integration, reducing redundant purchases.
  • Delivered key metrics and recommendations to stakeholders on vendor data through questionnaires, workshops and data gathering form 23+ different Nasdaq teams for source optimization.
  • Automated web scraping of 300+ confluence pages to extract vendor data and build a visual stream map for the Index business.
  • Boosted collaboration and auditability by implementing accelerated Git change-log for a large codebase maintained by 120+ analysts, enhancing change management by 30%.

Researcher/Developer, Intership

Indian Space Research Organisation (ISRO), RRSC Bengaluru | Jan 2018 - Feb 2019

  • Conducted comprehensive research on big data technologies and machine learning frameworks, with a focus on Hadoop and Apache Spark, performing comparative analysis using test datasets to evaluate performance metrics.
  • Analyzed and evaluated multiple machine learning models, ultimately selecting the SVM algorithm for its efficacy in image classification tasks.
  • Leveraged Apache Spark's distributed computing capabilities to process a large-scale image dataset, comprising approximately 1tb+ geographical satellite images.
  • Implemented a Naive Bayes classifier using Java on the Spark framework, optimizing for parallel processing and achieving a 30% reduction in computation time compared to traditional methods.
  • Developed a binary classification model to categorize images into landscape classes, achieving an accuracy rate of 85% on the test set.
  • Processed and analyzed image data, utilizing Spark's MLlib for feature extraction and model training, resulting in a 40% improvement in data processing efficiency.
  • Engineered a scalable pipeline capable of handling up to 100 images per minute during the classification process, demonstrating the system's potential for real-time applications.
  • Implemented cross-validation techniques, resulting in a 10% increase in model robustness and generalization capabilities across diverse facial datasets.
  • Utilized Spark's RDD (Resilient Distributed Dataset) to parallelize data processing across a cluster of 5 nodes, achieving a 5x speedup in overall computation time.
  • Conducted performance tuning of the Naive Bayes model, optimizing hyperparameters to improve classification accuracy by 7% compared to the baseline model.

iOS App Developer, Intership

Monkfox, Bengaluru | June 2017 - Feb 2018

  • Developed an iOS app using Swift and XCode with user authentication, order management, and checkout functionalities.

Education

University of Hertfordshire

Master's in Advanced Computer Science | Distinction

Relevant Courses: Foundations of Data Science, Neural Networks and Machine Learning, Computational Algorithms and Paradigms.

Visveswaraya Technological University (VTU)

Bachelor's in Computer Science Engineering

Relevant Courses: Data Structures, DBMS, Linear Algebra & Calculus, Computer Networks, Operating Systems, Software Engineering.

Projects

RAG-LLM Based Financial Document Parser | ONGOING

Streamlined a local LLM with RAG-based scanning solution using Ollama, achieving 90%+ accuracy in identifying regulatory risks in financial documents. Automated document analysis and risk highlighting with local LLM and retrieval-augmented generation workflows, enhancing compliance review efficiency by 3x.

View Project

Art Gallery Web Application

Built a database system to manage artwork and artist details using HTML, CSS, PHP, and Triggers for data manipulation.

View Project

Mushroom Disease Detection Android App

Developed an Android app that uses Naive Bayes and OpenCV for mushroom disease detection based on images captured by cellular devices.

View Project

Deep Feature Extraction with Autoencoders and Semi-Supervised Image Classification

Designed a semi-supervised image classification model leveraging autoencoders for feature extraction and support vector machines (SVM) for classification.

View Project

Credit Card Fraud Detection

Built 5 different ML models using Python and compared multiple algorithms (Linear Regression, Logistic Regression, XGBoost, Decision Tree, Random Forest, SVM) for performance against credit card transaction data.

View Project

Blog

Prompt Engineering May 2025

Mastering Prompts: How I Get the Best Results from LLMs

Explore my techniques for crafting effective prompts for large language models. Learn how clarity, context, and constraints can help you get the best results from AI.

Read on Medium →

Hobbies

Contact

Email: chakravarthynidhi@gmail.com