Nishok Ilangovan

Passionate about uncovering patterns and delivering actionable insights.

Python • SQL • Snowflake • Power BI • Neo4j

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About Me

Over three years of experience in data analytics and machine learning
Worked across insurance, research, education and retail domains
Build dashboards, automate pipelines and develop predictive models
Proficient in Python, SQL, Snowflake and Power BI
Passionate about turning complex datasets into clear stories and collaborating with teams

Experience

Data Engineer

StableCoupons Inc.

Jan 2025 – Present

Built an end-to-end AWS data pipeline with S3, Lambda, and PySpark to process 2 years of financial data under a Medallion Architecture.
Automated incremental ingestion, validation, and backfill workflows with centralized logging and schema tracking
Developed predictive analytics dashboards in Tableau to visualize stock trends and forecasting insights.

Data Engineer

JA ASSURE

Jun 2024 – Dec 2024

Built a RAG-based FAQ chatbot with Whisper AI, automating responses and reducing analyst workload.
Engineered PDF-to-dataset extraction pipelines, improving model training efficiency by 50%
Developed premium prediction models with XGBoost to optimize underwriting decisions

Business Intelligence Engineer

University of Maryland

Jan 2024 – Dec 2024

Led the migration of approval workflows from Qualtrics to Salesforce, saving 150+ staff hours per semester
Designed ERDs, DFDs, and system specifications to enhance data accuracy and ERP integration
Delivered process automation and documentation that reduced manual errors by over 30%

Data Engineer

InterviewDesk

Jan 2024 – Jul 2024

Developed data pipelines using Python, Django and Node.js to collect and process candidate data.
Created dashboards and reports to track recruitment metrics and candidate engagement.
Applied machine learning techniques to enhance candidate matching algorithms.

Business Intelligence Developer

WNS Global Services

Oct 2021 – May 2023

Analyzed large datasets to provide business insights for clients in various industries.
Developed ETL processes and interactive dashboards to support decision making.
Presented data‑driven recommendations to senior leadership.

Machine Learning Intern

Genik Technologies

Aug 2021 – Oct 2021

Developed CNN and YOLOv4 models for soil classification using ImageNet datasets
Applied transfer learning with ResNet to enhance accuracy and optimize irrigation schedules
Developed image classification models using Python and OpenCV.

Featured Projects

Capstone Project for Extended Studies Department, University of Maryland

Jan 2024 – Dec 2024

Automated the course renewal request process for faculty using Salesforce, addressing a 30 % increase in request volume and reducing processing time by 40 % in the first quarter.
Led system analysis by creating Data Flow Diagrams (DFD) and Entity Relationship Diagrams (ERD) to map data processes, improving data accuracy by 25 % and enabling the system to handle all requests.
Produced detailed system specifications and Salesforce integration strategies, reducing processing errors by 30 % and doubling request handling capacity.
Implemented a new information system on time with 100 % of requirements met, increasing faculty satisfaction scores by 20 %.

ScrumKanbanJiraSalesforceSystem Analysis

Video Games Sales & Success Prediction using PySpark

Jan 2024 – Mar 2024

Executed comprehensive feature engineering on Steam video game data for predictive modeling.
Developed a predictive model with 81 % accuracy using Random Forest and Spark GraphFrames.
Applied GraphFrame analysis to identify relationships between games, developers and genres, improving recommendations for marketing strategies and inventory optimisation.

PySparkRandom ForestGraphFramesModel Building

Crime Analysis and Projection of Crime Prevention Costs in Los Angeles

Aug 2023 – Dec 2023

Scraped crime data from the state website of California; cleaned, processed and merged datasets for analysis.
Used pandas to analyse crime statistics and identify key trends driving crime rates in Los Angeles, producing a detailed report.
Created visualisations with Plotly, Folium, Matplotlib and Seaborn, revealing a 15 % increase in property crimes and a 10 % decrease in violent crime rates over the past decade.
Trained predictive models using XGBoost and linear regression to forecast crime prevention costs, accurately predicting required budget increases to maintain reduction rates.

PandasScikit‑LearnXGBoostData VisualisationPlotly

Smith Seekers Inc Research Database

Aug 2023 – Nov 2023

Extracted and compiled data from ranking websites to analyse program ranking trends for a graduate business school.
Built a search‑engine‑style database for prospective students, compiling program details such as tuition, credit requirements and duration.
Designed entity‑relationship diagrams and database schemas using Lucidchart and implemented queries with SQL.

SQLOracleMySQLDatabase Design

Image Classification of Dry and Wet Soil

Dec 2022 – May 2023

Developed a convolutional neural network with OpenCV to classify soil images into wet and dry categories for agricultural applications.
Achieved 92 % accuracy in distinguishing between wet and dry soil images.
Optimised irrigation schedules in test areas, resulting in a 30 % reduction in water usage by applying model predictions.

OpenCVDeep LearningCNNPython

Predictive Analysis of Life Expectancy

Dec 2022 – May 2023

Predicted life expectancy trends from 2000 to 2015 by incorporating immunisation, economic and health data into a single model.
Used R Studio and programming for data analysis and visualisation, identifying a strong correlation (r = 0.85) between immunisation rates and life expectancy.
Integrated socio‑economic factors into the model and provided recommendations that could increase life expectancy by up to five years in some countries.

R StudioData AnalysisVisualisationMachine Learning