Mridul Gulati

Data Scientist

India2 yrs 9 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Engineered high-throughput data pipelines for LLMs.
Achieved 95%+ accuracy in ML model training.
Developed scalable solutions for fintech applications.

Stackforce AI infers this person is a Data Scientist with expertise in Machine Learning and Data Engineering for Fintech and AI industries.

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Skills

Core Skills

Machine LearningData EngineeringCloud ComputingData Science

Other Skills

API DevelopmentASP.NETAWSAWS SageMakerAlgorithmsAmazon Web Services (AWS)AnalyticsApplied Machine LearningApplied SciencesArtificial Intelligence (AI)BERT (Language Model)CascadingCluster AnalysisCredit ScoringCritical Thinking

About

Hi there! 👋 I'm Mridul. I have over 1.5+ year of work experience, currently working as a Data Scientist at Turing. I was enrolled in the Amazon ML Summer School 2024. I have experience working in Backend, Big Data, Machine Learning and Full Stack Development. During my Software Engineering career, I have worked with multiple programming languages, libraries, frameworks and technologies such as Python, Django/Flask, SQL, Pytorch, React, Kubernetes and AWS. I love competitive programming, and solving complex problems, I am rated 1540 on LeetCode, and I am currently 2* on Codechef. My interests range from distributed systems, big data to AI, and I'm always eager to explore new technological horizons. On a personal level, I'm passionate about self-improvement and personal growth. I believe that we can always learn and grow, both professionally and personally. I love reading books 📗 and listening to podcasts 🎙️ I hope you have a lovely day!

Experience

2 yrs 9 mos

Total Experience

1 yr 4 mos

Average Tenure

1 yr 9 mos

Current Experience

Turing

Data Scientist

Sep 2024 – Present · 1 yr 9 mos · United States · Remote

Engineered high-throughput data pipelines using Python and AWS (S3, Lambda, SQS) to prepare multi-terabyte
training datasets for Microsoft’s LLMs. Optimized preprocessing logic and I/O bottlenecks, increasing pipeline
throughput by 40% and reducing model training latency by 15%.
Automated insurance document processing for Two Sigma using OCR (Tesseract), custom rule-based logic, and ML
enrichment models to extract structured data from complex PDFs. Reduced underwriting workload by 85% and
accelerated quote generation by 4×.
Designed and deployed a cloud-native system (AWS Lambda, RDS, Redshift) for ingesting and parsing thousands
of unstructured PDF documents daily. Improved policy premium accuracy by 12%.

PythonAWSOCRMachine LearningData Engineering

Amazon

Amazon ML Summer school

Jul 2024 – Aug 2024 · 1 mo · India · Remote

Completed an intensive ML training program focused on supervised learning, deep learning, and model evaluation.
Implemented and fine-tuned models using scikit-learn, XGBoost, and PyTorch, achieving 95%+ accuracy on
classification tasks with structured and unstructured datasets.

Supervised LearningDeep LearningModel Evaluationscikit-learnXGBoostPyTorch+2

Chainaware.ai

Machine Learning Engineer Intern

Aug 2023 – Aug 2024 · 1 yr · Zurich, Switzerland · Remote

Built the Rugpull Detector for Solana using labeled blockchain transaction data and XGBoost, achieving a 0.96 F1
score in production. Work directly contributed to successful VC funding and MVP adoption.
Deployed a suite of ML agents for credit scoring, fraud detection, and wallet audits. Architected scalable pipelines
with real-time inference, strengthening ChainAware’s platform reliability and product-market fit for DeFi clients.

XGBoostMachine LearningCredit ScoringFraud DetectionData Engineering