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Deepanshu NegiDN
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Deepanshu Negi

@deepanshunegi

Data Scientist specializing in production-ready ML and GenAI automation for healthcare insurance.

India
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What I'm looking for

I’m looking for a role where I can work on impactful data/ML problems, build scalable production solutions, and continue growing technically. I value strong engineering culture, ownership, collaboration, work-life balance, flexibility, and opportunities to learn and contribute at scale.

I’m a results-driven Data Scientist with 6+ years of experience delivering end-to-end machine learning solutions across healthcare insurance, life sciences, and digital platforms. I build scalable data products and automate complex workflows to extract actionable insights from structured and unstructured data, with a strong focus on production-grade deployments.

Recently at Tiger Analytics, I designed and deployed an automated pet insurance claim adjudication pipeline using OCR, Azure Durable Functions, and GPT-based GenAI—improving auto-adjudication from 19% to 60%. I also led GenAI integration for document ambiguity, boosting adjudication confidence by 25%, and built invoice extraction workflows to improve speed and accuracy; earlier, at OPTUM/UnitedHealth Group, I delivered real-time recommender systems (CTR +20%), automated RTO scoring (manual effort -50%), and a delivery capping model projected to save ~$2M.

Experience

Work history, roles, and key accomplishments

Tiger Analytics logoTA
Current

Data Scientist

Jul 2024 - Present (1 year 11 months)

Designed and deployed an automated pet insurance claim adjudication pipeline using OCR, Azure Durable Functions, and GPT-based GenAI, increasing automation from 35% to 50%. Improved invoice extraction speed/accuracy, automated policy-pet matching for deductible/co-pay rules, and increased adjudication confidence by 25% via GenAI handling of document ambiguity.

Optum logoOP

Associate Data Scientist

Feb 2022 - Jun 2024 (2 years 4 months)

Built and deployed ML solutions for healthcare insurance workflows, reducing manual effort and improving decision-making accuracy. Developed targeting and real-time recommender systems (CTR +20%), automated RTO scoring (manual effort -50%), created a delivery capping model with ~$2M projected savings, and implemented NLP classification to extract behavioral insights.

TS

System Engineer (ML)

Aug 2019 - Feb 2022 (2 years 6 months)

Delivered production ML systems for Life Sciences clients, collaborating on requirements, technical architecture, model development, and deployment using Agile practices. Applied regression/classification and statistical analysis (including EDA and tuning) to predict suitable patients for clinical trials, and used PCA/t-SNE and clustering to derive insights from large biomedical datasets.

Education

Degrees, certifications, and relevant coursework

GT

Galgotias College of Engineering & Technology

Engineering in Information Technology, Information Technology

2015 - 2019

Grade: 72.9%

Studied Engineering in Information Technology from 2015 to 2019, achieving a score of 72.9%.

SM

St. John’s Sr. Sec. School, Meerut

Intermediate, Intermediate

2013 - 2014

Grade: 90%

Completed Intermediate education from 2013 to 2014 with a score of 90%.

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