JASPREET SINGH

Senior Data Scientist, Maaloomaatiia Arabian Gulf

ai.jaspreetsingh@gmail.com • +91-9780-9535-56 • linkedin.jaspreetblogs.in

github.jaspreetblogs.in • blog.jaspreetblogs.in

Executive Summary

Lead Data Scientist with 6+ years of experience in building advanced analytical tools for Fortune100 companies like Walmart, JP Morgan, Emirates Global Aluminium, Dubai Department of Economy and Tourism, lululemon, etc. Responsible for project planning and execution, team management, risk and issue management.

Work Experience

Lead Data Scientist | Maaloomatiia

Sep2023 - Present
Consumable Energy Prediction

  • Dubai Metro faced challenges in accurately forecasting water, AC & DC electricity, and cooling consumption for the Green and Red lines, leading to frequent resource shortages and excesses
  • Leveraged N-BEATS algorithm to enable Dubai Metro with insights on precise & reliable consumption demands
  • Yielded savings of approximately $80K annually by reducing stagnation and resource outages, while simultaneously meeting sustainability goals
  • Tools: Python, Dataiku, NeuralForecast

Cab Complaint Classification

  • The Road and Transport Authority (RTA) customer service webpage allowed users to share complaints about cab services and RTO experiences, which were manually routed to the appropriate department, service, or touchpoint by the back-end team for resolution
  • Delivered a Llama3-8b-supported NLP model combined with an XGBoost classifier that can effectively handle multi-hierarchical classification of user complaints for quicker resolution
  • The model resulted in a 66.67% reduction in complaint acknowledgment lead time while maintaining 72%-89% accuracy in model performanceF
  • Tools: Python, Dataiku, Ollama, Llama3-8b, XGBoost

Overnight Visitor Arrivals and Spend Prediction

  • Dubai government's Department of Economy & Tourism (DET) wanted to understand the factors that lead to changes in overnight visitor footfall and how to predict accurate arrivals from each source market
  • Developed an XGBoost-based solution equipped with What-If analysis tool to understand the factors leading to overnight visitor arrivals and the economic impact they generate for the Dubai government
  • The model was used by the CEO of DET and government body members to re-evaluate global marketing expenditure and experiment with other tourism-related factors to channel demand based on needs
  • Tools: Python, Dataiku, Databricks, SciKit-learn, NBEATS, PatchTST, Streamlit

Social Media Analysis and Insights

  • Dubai's immigration services faced challenges in gauging public opinion, identifying key themes, and responding to emerging issues
  • Implemented advanced NLP techniques for sentiment analysis, LLM-based text categorization, and summarization to extract insights from social media posts and public feedback, enabling efficient data-driven decision-making
  • Improved operational efficiency by tracking public service KPIs and providing timely insights into public sentiment, helping the GDRFA address issues more effectively, prioritize service improvements, and enhance customer satisfaction
  • Tools: Tech Stack Used: Python, Dataiku, Ollama, Llama2-3b, Generative AI

SKU Purchase Prediction

  • One of the Fortune 500 sportswear retail organizations faced challenges in accurately predicting SKU purchases, leading to frequent stockouts and overstock situations, causing significant revenue losses and storage costs
  • Developed a Temporal Fusion Transformer (TFT)-based advanced time series model to deliver precise and reliable SKU purchase forecasts
  • The predictive model implementation resulted in 40% reduction in stockouts and overstock situations, saving approximately $560k annually and enhancing inventory management and operational efficiency
  • Tools: Tech Stack Used: Python, Datarobot, TensorFlow, Keras

ATM Cash Withdrawal Prediction

  • Arab Bank wanted to improve the customer service satisfaction by reducing the Out-of-Cash scenarios in their ATM through out the Egypt and Jordan network
  • Developed a cash withdrawal demand prediction model using Random Forest regressor which assisted operations teams to proactively look out for potential Out-of-Cash situations
  • The ATM Cash demand forecasting model was used by both operation team and ATM vendors to monitor and replenish the flagged ATMs
  • Tools: Tech Stack Used: Python, XgBoost, LGBM

Senior Data Scientist | J.P. Morgan

Feb2022 - Sep2023
Attention & Deep Learning Based Email Classifier

  • The Service & Implementation team had to go through hundreds of incoming emails a day to comprehend and tag the relevant team for issue resolution
  • Implemented an attention-based neural network using TensorFlow to understand and classify emails into appropriate categories and teams
  • The solution reduced the manual labor required to identify and tag emails to teams for more than 150 mailboxes
  • Tools: Tech Stack Used: Python, Tensorflow, JupyterLab, AWS

Classification Based Analogous Client Finder

  • Currently numerous billing platform are used by JP Morgan's commercial banking unit, tailoring to client's specificity which adds extra burden on operations and maintenance cost
  • Proposed XGBoost based classification solution lead to operational savings in production deferral costs through strategic client migration
  • Provided solution was used to segregate for migration plan
  • Tools: Tech Stack Used: Python, Tableau, Scikit-Learn

Latent Dirichlet Allocation Based Request Categorization

  • Eliminated Onboarding & Servicing team's invisibility to unseen trends and change in key issue subjects
  • Developed Latent Dirichlet Allocation based topic modeling & pattern recognition, leading to automated category and subject identifier
  • Transformed solution was adopted by Commercial Banking's client service team to understand change in client issues to improve process optimization
  • Tools: Tech Stack Used: Python, NLTK, Sklearn

Data Scientist | Mu Sigma

Dec 2018 - Jan 2022
Random Forest Based Manufacturing Halts Reduction

  • Emirates Global Aluminum (EGA) was facing multiple frequent production halts due to machinery faults which would last for more than weeks and cost multiple millions dollar in loss of opportunity
  • Assisted EGA to reduce unplanned maintenance shutdown, production halts and improve equipment life cycle by predicting potential machinery failures well in advance
  • Proposed Random Forest based predictive solution lead to operational savings of over millions annually in production deferral costs
  • Tools: Tech Stack Used: Python, Scikit-Learn, Azure DataBricks, Tableau

Computer Vision & Deep Learning Based Brick & Mortar Customer Analysis

  • Understanding offline customer's behavior patterns to make better decisions in store operations (staff management, product placements, etc)
  • Implemented in-store video analytics solution using Single shot multibox detector (SSD) based YOLO V3 solution
  • Delivered hour level data about customer entry, exit & in-store count, number of aisle visits, traditional checkout counter count, Scan-&-Go counters encouragement and others
  • Tools: Tech Stack Used: Python, YOLO v3, OpenCv

Natural Language Processing Based Early Trends Detector

  • Eliminated sourcing & procurement team's invisibility to unseen trends
  • Developed Natural Language Processing based model lead to 3 fold decrease in Out-of-Stock scenarios
  • Transformed solution was adopted by clients as their official banner product for 2019 Black Friday Sale
  • Tools: Tech Stack Used: Python, NLTK, Tableau

HOBBY PROJECTS

Technical Skills

Machine Learning: Linear Regression, Logistic Regression, SVM, KNN, Decision Tree & Random Forest, Ensemble Models
Deep Learning: Convolutional Neural Network(CNN), RNN, Attention, BERT, Natural Language Processing, Computer Vision
Tools: TensorFlow, Keras, Pandas, Numpy, Matplotlib, Seaborn, scikit-learn, ARIMA, Prophet, NLTK, OpenCV, Yolo V3, Databricks, Dataiku, Datarobot, Jira, Kanban Board
Misc: Git, GitHub, Python, SQL, Probability, Statistics

Education

BITS Pilani- Master of Technology (M.Tech) in AI&ML
LPU: Bachelor of Technology (B.Tech) in ComputerScience

Achievements

Rising Star- Commercial Bank - JP Morgan 2022
MAGniverse: In-house Decision Making Framework deliverd from ideation to production