Operations Analytics

RAJAT BAJAJ

Durham, NC LinkedIn: rajatbajaj** 919-***-**** *****.*****@****.***

SUMMARY

Data Analytics pro with 3+ yrs experience optimizing complex SQL workflows, automating reporting and crafting Tableau/Python dashboards that drive operational efficiency. Adept at defining KPIs, collaborating cross-functionally and turning data into impact. Languages & Tools: SQL, Python, R, Git, JIRA, Advanced Excel BI & Data Platforms: Tableau, Looker, BigQuery, Snowflake, Redshift, PostgreSQL, AWS Modeling & Analytics: A/B Testing, Data Modeling, Predictive Modeling, scikit-learn, XGBoost, PySpark, ETL EDUCATION

DUKE UNIVERSITY, The Fuqua School of Business Durham, NC Master of Science in Quantitative Management: Business Analytics, Strategy Track May 2025 Domain Coursework: SQL, Python Programming, R Programming, Data Visualization, Data Science, Deep Learning, Operations Analytics NORTHCAP UNIVERSITY Gurgaon, India

Bachelor of Technology, Computer Science Jun 2020

Co-founder "Yes U May Speak" Literary Society, Choreographer: Morphinators Dance Society, “Mr. Fresher”, American Society for Quality WORK EXPERIENCE

NUTANIX, Support Operations Bangalore, India

Operations Engineer II Mar 2022 – Jun 2024

● Designed and automated Employee Management dashboard (SQL, Tableau), to monitor KB/SR KPIs for 150+ staff and saving 700+ manager hours annually via strategic process enhancements

● Automated Quarterly Business Review report (SQL, Python, Tableau) to track Time to Resolution(TTR) across all internal products, reducing manual operations effort from 5 hours to 10 minutes saving 160 hours annually

● Authored BRD and partnered with DevOps & Data Science to redesign global, skills-based call routing, reducing average customer wait times by 30% and improving SLA adherence

● Managed Support Contract Extensions by tracking key contract metrics, streamlining ~50 monthly renewal decisions, saving 50 hours annually and preserving $3M+ in recurring revenue

● Facilitated bi-weekly Agile sprint meetings for DevOps team of 12 cross-functional members, overseeing JIRA prioritization, task assignment and backlog grooming, to ensure alignment across PMs, engineers, and data teams

● Resolved 30+ ad-hoc JIRA requests via optimized SQL queries or Tableau dashboards, reducing follow-up requests by 20%

● Developed optimized SQL workflows to redesign existing Tableau dashboards with enhanced filters, layouts, and drill-downs, reducing support tickets by 20% and tripling adoption across product and support teams LANDMARK GROUP, Competitive Intelligence at Data Labs Bangalore, India Business Analyst May 2021 – Mar 2022

● Spearheaded Competitive Intelligence Solution to assess market dynamics and optimize pricing strategy for 3 Ecommerce verticals delivering end-to-end analytics from data scraping to price recommendations

● Built weekly-price forecasting model using historical trend and elasticity analysis across 10,000+ SKUs, enabling data-driven pricing decisions that increased off-season profits by 6.5%

● Delivered daily forecasting reports during White Wednesday campaign, partnering with Pricing and senior leadership to implement real-time price adjustments that drove 15% campaign sales over target

● Collaborated with Pricing and Merchandising Teams to refine and deploy pricing logic, influencing price decisions for ~30% of all SKUs and support dynamic pricing and preserve margin targets

● Automated web scraping across 3 competitor websites using BeautifulSoup, Selenium, Scrapy, and Requests, reducing manual data collection by ~90% and improving data freshness for weekly pricing decisions

● Led pre- and post-analysis meetings with senior leadership and non-technical stakeholders, driving iterative improvements to pricing and assortment decisions across respective product categories

SELECTED PROJECTS

Forecasting Competitor Pricing Trends (Python, R, ARIMA, TBATS). Analyzed and cleaned 20M+ rows of SKU-level data to build seasonal time series models, identified competitor pricing patterns across 3 major players, and delivered strategy recommendations for 80K+ products to senior leadership to inform assortment and margin decisions. Predicting Loan-Default Risk (R, Python, ResNet, MLP). Built full pipeline on 255,347 loans; baseline regression returns raised by 4%, followed by deep-learning models (ResNet & MLP) to reach 86% accuracy, outperforming XGBoost and Random Forest Analyzing Malware of APKs (APKTool, Weka, and Python: os, glob, ElementTree, csv). Built classification models to achieve accuracy of 97.52% as Student Research Associate at Indian Institute of Technology (IIT, Kanpur).

ADDITIONAL INFORMATION

Interests: FC Barcelona fan, host of The Bodhi Tree podcast on Spotify, world cinema and film reviewing: David Fincher, Wong Kar- wai, solving crosswords, fingerstyle guitar, WWII & Cold War history, sports analytics; play basketball, roller hockey, badminton

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