Shaurya Rawat
Senior Consultant @EY | Past: UKG, Deloitte, Cognizant | Data & ML Engineer | Data Science & Strategy
- Role
- Senior Consultant 3 - Data & Ai Strategy at EY
- Location
- New Delhi, DL, IN
- LinkedIn followers
- 500 followers
About Shaurya Rawat
6 yearsAs a data-driven professional, I specialize in Data Engineering, Machine learning and business data analysis. I like to be fast, versatile and excel in multiple technologies and businesses: Finance, Payment, Life Science, Retail : Communication | Presentation :Data Engineering | Machine Learning | Data Strategy | Data Visualization | Data Warehousing | Data management | Data Governance | Data Integration | Data and Application Architecture :Supervised (Linear Regression, Decision Trees), Unsupervised algorithms (KMeans, DBScan), Ensemble methods (Random Forest, XGBoost, GradientBoost, AdaBoost ), Neural Network, Transformers, Hugging Face, Hyper-parameter tuning, Tokenisation, NLP, EDA:Databricks | Azure (ADF, Synapse, SQL Server, Event Hubs, Stream Analytics)| Cloudera (CML, CDE)| AWS (Glue, Athena, S3, DynamoDB)| Cassandra NoSQL | Snowflake | Tableau | Power BI | ExcelLanguage: SQL and Python (Pyspark):1. Microsoft Certified Data Engineer Associate2. Databricks Certified Data Engineer Associate 3. Reltio Certified Solution Architect 4. Tableau Certified Consultant5. Tableau Certified Data Analyst 6. Salesforce Certified Associate 7. Microsoft Certified Azure Fundamentals8. Gen-AI Fundamentals As an artist, I have passion for Designing, Singing, Acting and ModelingBecoming student for life and honing skills to be a leader with knowledge, confidence and empathy.
Experience
Senior Consultant 3 - Data & Ai Strategy
Jan 2026 — Present · Gurugram, IN
Finance, Payments : Senior Data Scientist- Improving Mastercard\'s CNP (Card-Not-Present) approval rates by identifying declines using ML pattern discovery model and explanability- Analyzing various issuers response codes (Card Restricted, Stolen, Lost, etc) to uncover actionable insights in payment authorization failures- Building scalable data pipelines and feature engineering workflows using Spark and Impala on high-Volume transaction data : Cloudera Machine Learning, CDE, Hadoop, Impala, Spark, Databricks, Azure, Tableau: SQL, Python, PySpark
Education
Great Lakes Institute of Management
Post graduate program, Artificial Intelligence and Machine Learning
The University of Texas at Austin
Post Graduate Program, Artificial Intelligence and Machine Learning
Deakin University
Masters in Data Science, Artificial Intelligence and Machine Learning, Data Science
DreamZone School of Creative Studies
diploma (Morning Batch), graphic design
Kendriya Vidyalaya
10th, Science
DIT UNIVERSITY
Bachelor of Technology - BTech, Computer Science and Engineering
Kendriya Vidyalaya
12th, Science
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