Aakarsh Arora

Data Scientist

Boston, Massachusetts, United States0 mo experience

AI EnabledAI ML Practitioner

Key Highlights

Developed innovative AI solutions for real-world impact.
Authored multiple research papers in machine learning and healthcare.
Collaborated on scalable AI projects enhancing user experience.

Stackforce AI infers this person is a Machine Learning Engineer with a focus on AI-driven solutions in Fintech and Healthcare.

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Skills

Core Skills

Data ScienceMachine LearningAwsBackend DevelopmentResearchGame Development

Other Skills

Python (Programming Language)Large Language Model Operations (LLMOps)Amazon Web Services (AWS)AlgorithmsDeep LearningUnreal engine 5Data AnalysisData MiningStatistical Data AnalysisDataPandasNumPyHuggingFaceApplication Programming Interfaces (API)Transformers

About

I’m an AI enthusiast, problem-solver, and lifelong learner currently pursuing my Master’s in Artificial Intelligence at Northeastern University. My passion lies in building AI-driven solutions that bridge the gap between research and real-world impact. Whether it's optimizing large language models for real-time applications, designing attention-based models for medical imaging, or diving deep into transformer architectures, I thrive on tackling complex challenges with innovative approaches. Beyond just algorithms and code, I’m driven by the bigger picture—how AI can create meaningful change. I enjoy collaborating with like-minded professionals who share the vision of building scalable, ethical, and transformative AI solutions. If you're working on something exciting in machine learning, NLP, or AI deployment, I'd love to connect and exchange ideas. Let’s push the boundaries of what AI can do, together!

Experience

0 mo

Total Experience

Average Tenure

Current Experience

Fidelity investments

Data Scientist

Jul 2025 – Dec 2025 · 5 mos · Boston, Massachusetts, United States · Hybrid

1. Built and evaluated an LLM-based document quality validation framework on 1,300+ financial documents, analyzing extraction accuracy under noisy and incomplete ground truth conditions.
2. Designed custom document-level and field-level evaluation metrics beyond exact matching, accounting for formatting variation, missing fields, and contextual correctness to better reflect real-world data quality.
3. Conducted detailed error analysis to identify label noise, reference-data inconsistencies, and systematic failure patterns across borrower, account, and payment fields.
4. Compared and benchmarked multiple pipeline strategies, including OCR-based extraction, ontology-guided prompting, and direct document parsing via LLM APIs, highlighting trade-offs in accuracy, robustness, and scalability.
5. Collaborated closely with cross-functional stakeholders to translate ambiguous business requirements into measurable ML evaluation criteria and actionable insights.
6. Documented findings and recommendations to support model iteration, validation, and production readiness within an enterprise environment.

Data SciencePython (Programming Language)Machine Learning

Pookie

Machine Learning Intern

Apr 2024 – Jun 2024 · 2 mos · New Delhi, Delhi, India · On-site

Deployed and trained LLM models with custom dataset on AWS SageMaker using SFTTrainer
Engineered features from large unstructured text datasets and conducted quantitative analysis,
Programmed APIs for enabling seamless deployment and improving content generation efficiency by
25%.
Collaborated with teams to deploy models at scale, ensuring efficient real-time data handling for over
10,000 daily users.

Large Language Model Operations (LLMOps)Amazon Web Services (AWS)Machine LearningAWS

Hexbit

ML Backend Intern

Mar 2023 – May 2023 · 2 mos · Delhi, India · Remote

Developed and deployed RESTful APIs for real-time SQL data integration, enhancing system efficiency
by 20%.
Collaborated with data scientists to design and implement scalable machine learning pipelines,
ensuring seamless integration into production systems.
Implemented JWT-based authentication mechanisms to secure AI models, reducing login errors by
15%.

AlgorithmsMachine LearningBackend Development

School of computer engineering, kiit du

Research Assistant

Feb 2022 – Jan 2023 · 11 mos · Bhubaneswar, Odisha, India · On-site

Authored and Co-authored 6 Research Papers published in reputed journals, addressing critical
challenges in machine learning, environmental safety, and healthcare analytics.
Pioneered Water Potability Analysis by implementing advanced machine learning algorithms, achieving
high AUC-ROC and F1 scores to predict water safety, contributing to public health advancements.
Engineered a Machine Learning Pipeline for Dementia Diagnosis, leveraging clinical datasets to analyze
disease progression, identify key biomarkers, and improve diagnostic accuracy.
Developed Computer Vision Solutions, including a semantic segmentation project to address real-world
challenges using cutting-edge techniques.
Enhanced Model Performance with Vision Transformers, incorporating data augmentation and
hyperparameter tuning to optimize outcomes.
Utilized Modern AI Frameworks such as TensorFlow, PyTorch, and HuggingFace Transformers, ensuring
scalable and efficient model deployment.
Advanced Data-Driven Insights by integrating statistical methods and predictive modelling to tackle
environmental and healthcare problems, providing actionable solutions.

Python (Programming Language)Deep LearningMachine LearningResearch

Vracademi

AI Game Developer Intern

Oct 2021 – Feb 2022 · 4 mos · Remote

Developed AI enemies using Unreal Engine 4’s AI system, enhancing player interaction and game difficulty through dynamic
enemy behaviours.
Partnered with teams to integrate scalable AI systems, enhancing gameplay complexity for 50+ multiplayer users.

Unreal engine 5Game Development