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subhadatta1

Subha D

@subhadatta1

Director Data Science

Stati Uniti
Inglese, Bengali, Hindi
Alcune informazioni sono riportate in lingua inglese.
Chi sono
I am a Director-level Data Scientist with over 15 years of experience applying statistical analysis, machine learning, and generative AI to solve complex business problems in insurance and finance. I specialize in developing mortality models, fraud detection solutions, and customer segmentation strategies while leading cross-functional teams to deliver actionable insights.... Continua a leggere

Competenze

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subhadatta1
Subha D
offline • 

Consulta i miei servizi

Consulenza in data science
I will analyze your data and build predictive ml and genai models

Esperienza lavorativa

Prudential

Prudential

Full time • 3 yrs 5 mos

Director, Data Science

Nov 2024 - Dec 20251 yr 1 mo

Led a team of 3 data scientists for the development of mortality models for Group Insurance Life and Disability customers. Created ML use cases and performed cost benefit analyses for stakeholders. Collaborated with Data Engineers, Machine Learning Engineers, Business, Actuary, and the Tech team to ensure smooth delivery of the developed solutions. • Led and developed response models for direct mail campaigns for Life Insurance products catered to Association members. • Managed multiple GenAI initiatives to effectively summarize medical records which save time for the Underwriters. • Led a team of 3 Data Scientists build a chatbot for answering questions related to Group Insurance underwriting policies, utilizing Retrieval Augmented Generation (RAG).

Lead Data Scientist

Jun 2022 - Oct 20242 yrs 4 mos

Oversaw the development of mortality models for Group Insurance Life and Disability customers. Worked with Machine Learning engineers in order to implement the XGBoost survival model in AWS. • Managed the team and project to train and build such solutions • Created model monitoring capabilities both for tracking model risk metrics and benefits generated from the model • Worked with Actuaries to effectively monitor model results and resolve any anomalies in a timely fashion