Taljinder Singh
ASU MS-DS '26 | 2+ Years Experience
Transforming raw data into actionable insights through machine learning, scalable cloud pipelines, and AI-driven solutions.
Transforming Data into Business Impact
Data scientist and ML engineer with a passion for building intelligent systems
My Journey in Data Science
I am a graduate student in Data Science at Arizona State University with 2+ years of professional experience in analytics and pricing strategy at OYO and Indus Insights.
My expertise spans machine learning, forecasting, optimization, and cloud-based data pipelines. I have a proven track record of designing scalable solutions that improved forecasting accuracy, optimized pricing models, and drove measurable revenue growth.
Core Competencies
Project Notebooks
Interactive data science workflows with code, visualizations, and insights
AI Indian Recipe Generator
Explored the use of Large Language Models (LLMs) for AI-driven recipe generation based on input ingredients.
# Fine-tuning LLMs for Recipe Generation from transformers import AutoModelForCausalLM, AutoTokenizer from peft import LoraConfig, get_peft_model import torch # Load base model (LLAMA3.2) model_name = "meta-llama/Llama-3.2-7B" tokenizer = AutoTokenizer.from_pretrained(model_name) # Configure QLoRA for efficient fine-tuning # Click "Run All" to see full project details
Book Recommender System
Developed a personalized book recommendation system using collaborative filtering techniques on the Book-Crossing dataset.
# Collaborative Filtering for Book Recommendations
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
# Load Book-Crossing dataset
ratings_df = pd.read_csv('BX-Book-Ratings.csv')
books_df = pd.read_csv('BX-Books.csv')
# Build user-item matrix for collaborative filtering
# Click "Run All" to see full project detailsHR Analytics Dashboard
Created a visual HR analytics dashboard using Tableau as part of coursework at Arizona State University.
# HR Analytics Dashboard - Data Preparation
import pandas as pd
import numpy as np
# Load HR dataset
hr_data = pd.read_csv('hr_dataset.csv')
# Analyze employee attrition patterns
attrition_rate = hr_data['Attrition'].value_counts(normalize=True)
# Prepare data for Tableau visualization
# Click "Run All" to see full project detailsYelp Arizona Analysis
Performed comprehensive analysis of the Yelp dataset for Arizona, focusing on restaurant businesses and user behavior.
# Yelp Arizona Analysis - PySpark Processing
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg
# Initialize Spark session
spark = SparkSession.builder.appName("YelpAnalysis").getOrCreate()
# Load Yelp JSON files (business, review, user, tip, checkin)
business_df = spark.read.json("yelp_academic_dataset_business.json")
# Filter for Arizona restaurants
az_restaurants = business_df.filter(col("state") == "AZ")
# Click "Run All" to see full project detailsProfessional Projects
Real-world solutions delivering measurable business results
Brent & Gas-Oil Spread Forecasting
Challenge
Energy trading desks rely on accurate short- to medium-term forecasts of commodity spreads for daily trading decisions, hedging strategies, and portfolio rebalancing. Existing Excel-based statistical models lacked the ability to capture non-linear dependencies between macro variables and price spreads, adaptability to shifting volatility regimes during global market fluctuations (e.g., OPEC decisions, inflation shocks), and automated performance monitoring.
Solution
Developed advanced LSTM-based forecasting framework to predict the spread between Brent crude oil and Gas-Oil prices. Aggregated 10+ years of daily data from multiple sources (EIA, Bloomberg, proprietary feeds). Engineered 30+ lag and trend features using temporal shifting, moving averages, and differencing. Implemented LSTM architecture in TensorFlow/Keras with grid-search tuning for sequence length, hidden units, dropout, and learning rate. Used walk-forward cross-validation to simulate production deployment and avoid look-ahead bias. Added model-drift monitoring pipeline for automatic retraining triggers.
Price & NPV Prediction Models
Challenge
Financial analysts needed standardized NPV estimation across verticals, accounting for real-time macro changes (inflation, interest rates, FX rates) while reducing human bias in manual spreadsheet forecasts. The client's decision pipeline lacked automation and cross-portfolio comparability, leading to inconsistent pricing strategies and delayed approvals.
Solution
Developed a comprehensive pricing and NPV prediction framework integrating macroeconomic indicators and deal-specific features. Engineered 20+ predictive features capturing macroeconomic trends (interest rate spread, CPI, PMI), industry-specific multipliers, and deal-level variables. Built feature store for reusable variables across business units. Implemented regularized regression (Ridge, Lasso) and tree-based ensembles (XGBoost, RandomForest) with cross-validated grid search. Automated pipeline for quarterly model retraining and deployed as Python microservice with REST API endpoints for real-time price recommendations.
Revenue Optimization Framework
Challenge
The existing pricing system at OYO relied heavily on manual analyst decisions, leading to inconsistent pricing across markets and property types, inability to adapt quickly to demand shocks (festivals, events, weather), and limited visibility into pricing rationale for on-ground teams.
Solution
Designed comprehensive pricing and revenue optimization framework for 100+ hotel properties. Integrated data from multiple sources: booking pace, competitor prices, search trends, and event calendars. Implemented ensemble forecasting pipeline combining ARIMA, Prophet, and Gradient Boosting Regressor to forecast occupancy and ADR at property and cluster level. Designed price optimization module using mathematical programming (SciPy's optimize.minimize) to maximize revenue subject to occupancy, min/max price bounds, and competitor constraints. Incorporated cross-elasticity to prevent cannibalization between nearby properties. Deployed output dashboards on Tableau and internal BI tools with explainability layer.
Professional Experience
2+ years driving impact through data, analytics, and innovation
Operations Support Specialist
What I Did
Streamline and automate student-admissions workflows for 10,000+ prospective students each cycle. Combine process optimization, CRM automation, data quality management, and analytics reporting to enhance applicant experience, improve operational throughput, and support staff efficiency across multiple campuses. Manage end-to-end admissions pipelines within Salesforce Education Cloud and Airtable, design and deploy automation workflows, generate KPI dashboards, and ensure FERPA-compliant data handling.
Key Achievements
Revenue Analyst
What I Did
Designed and implemented comprehensive pricing and revenue optimization framework for 100+ hotel properties across India and Southeast Asia. Combined dynamic demand forecasting with optimization algorithms to drive higher occupancy and yield. The framework integrated multiple data sources including booking pace, competitor prices, search trends, and event calendars to deliver ~10% uplift in revenue metrics (RevPAR & ADR) compared to static pricing.
Key Achievements
Associate
What I Did
Developed advanced analytics models and forecasting systems for financial markets and energy trading. Built pricing and NPV prediction frameworks for investment decisions, and LSTM-based forecasting models for commodity spread prediction. Combined deep learning, econometric indicators, and time-series validation techniques to improve forecast accuracy and interpretability across multiple business verticals.
Key Achievements
Skills & Technologies
Tools and technologies I use to build amazing things
Programming Languages
Building solutions with modern languages
Machine Learning
Creating intelligent systems
Data Analytics
Transforming data into insights
Cloud & Tools
Deploying at scale
Specializations
Domain expertise areas
By the Numbers
Let's Connect
Open to opportunities in Data Science, ML Engineering, and Analytics roles
Ready to Chat?
Whether it's a project, job opportunity, or just a friendly hello, I'd love to hear from you!
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