Shrey Verma
Data Scientist
ABOUT ME
Meet Shrey Verma, the data scientist that wears his love for numbers on his sleeve and his passion for health in his heart. With a stack of research papers on the ethics of AI by his side, Shrey's mission is to connect the world with data and change the game forever.
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But don't let his nerdy persona fool you - behind those glasses and lab coat, Shrey is a fierce problem-solver who never shies away from a challenge. After all, he lives by the wise words of Steve Jobs - "We are here to put a dent in the universe."​
When he's not crunching numbers and making scientific breakthroughs, you can find Shrey exploring new trails and cuisines, always on the lookout for his next adventure. Whether he's hiking up a mountain or coding his way to the top, Shrey is always up for a challenge - and always has a witty quip up his sleeve.
SKILLS
Programming Languages: Python, JavaScript, SQL, Java, C++, Haskell, Apex
Databases & Processing: PostgreSQL, MySQL, MongoDB, Apache Spark, Redis, PL/SQL
Packages & Tools: Docker, Kubernetes, Helm, Terraform, AWS EKS, Flask, FastAPI, RESTful APIs
Web Dev: React.js, Next.js, Express.js, HTML, CSS, TypeScript, Linux, Bash, Blockchain
Cloud & DevOps: AWS (EKS, Lambda, S3, RDS), GCP (Cloud Run, BigQuery), CI/CD
EXPERIENCE
July 2023 -July 2024
Ernst & Young (EY)
Tech Consultant, Senior Analyst
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Aided in fine-tuning EY’s internal LLM using OpenAI, integrating RAG with FAISS for real-time knowledge retrieval and incorporating user feedback loops, ensuring complete compliance on EY’s data security policies.
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Implemented an XGBoost model which was Dockerized and then deployed via FastAPI for inventory optimization for wire manufacturer, reducing POs and transportation costs for the client by 3%.
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Automated data pipelines using PL-SQL, and Oracle R12.2 for a bonded warehouse to enhance data capturing, tracking, and visualization, resulting in 3% savings in cash flow.
August 2022 -December 2022
Software Development Intern
ATMS Co and LLP
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Created a financial index for 4,000 MSMEs data stored in AWS S3 using Linear Regression, K-means clusters, and Random Forest to predict revenue trends and classify financial stability.
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Deployed the index with weighted metrics (e.g., revenue growth, profit margins), achieving 0.92 correlation with real world and 95% confidence, via MLflow on Google Cloud Platform (GCP) for MSME trend tracking.
April 2022 - July 2022
Data Analytics Intern
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Data Sutram
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Engineered data pipelines using Apache Kafka and Spark to integrate real-time satellite and alternative data, and developed Naive Bayes models to identify optimal locations for drugstore openings.
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Deployed models via CI/CD pipelines with Jenkins on AWS EC2 and visualized data using SQL and Tableau, achieving an 18% sales boost in pinpointed stores.
June 2021 -April 2022
Data Engineer Intern
RD&X Network
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Engineered data pipelines using Apache Kafka and Spark to integrate real-time satellite and alternative data, and developed Naive Bayes models to identify optimal locations for drugstore openings.
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Deployed models via CI/CD pipelines with Jenkins on AWS EC2 and visualized data using SQL and Tableau, achieving an 18% sales boost in pinpointed stores.
Have partnered with
Projects
1
Influenza Vaccination Coverage | Pfizer AI Team
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Built a full-stack dashboard with Express.js and React.js, deploying optimized Random Forest and CatBoost models for influenza vaccination analysis, achieving 94% R2, and refining Pfizer’s campaign strategies.
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Conducted large-scale data preprocessing, feature engineering, and handled class imbalance leveraging SMOTE and ADASYN, enabling the identification of key demographic trends that led to a projected $150k in additional revenue.
2
Investment Recommendation Model | JPMorgan Wealth Management
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Built and deployed a pairwise neural network with k-core extraction using Flask and AWS EKS for startup-investor recommendations, improving accuracy by leveraging funding type and investment history.
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Delivered data-driven insights to improve investor relations and competitive positioning by achieving 93% accuracy and 98% precision, enhancing client satisfaction with tailored startup recommendations.
3
Hospital Mortality Forecasting
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Developed and deployed a mortality prediction model based on patient-doctor interactions using MIMIC-III dataset, leveraging XGBoost and Random Forest, and deployed it using Azure Kubernetes Service (AKS).
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Optimized model performance with 5-fold cross-validation, hyperparameter tuning, and class imbalance handling, achieving an ROC-AUC of 0.95 and an F1-score of 0.93.
4
Human Protein Atlas CV
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Developed a CNN ensemble for protein localization utilizing NFNet L2 and a modified ResNet 200d with CBAM, achieving 0.62 mAP and 0.70 F1 score.
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Designed and implemented a HIPAA compliant, production-grade, real-time federated learning framework deployed through AWS EKS.