Kamal Pangeni
Data AnalystAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Applied AIML Lead @ JPMorganChase | PhD, Machine Learning
As a Senior Data Scientist at CVS Health, I leverage my PhD in Physics and my expertise in Machine Learning, Deep Neural Networks, and Data Analysis to create innovative solutions for complex healthcare challenges. I have over 5 years of experience in developing and deploying predictive models, interactive dashboards, and custom visualizations for various business and clinical use cases. My passion is to use data and analytics to drive operational efficiency, profitability, and quality of patient care. I enjoy collaborating with cross-functional teams, stakeholders, and end-users to understand their needs, provide insights, and deliver value. I am always eager to learn new skills, tools, and technologies to enhance my capabilities and expand my knowledge.
Washington University in St. Louis
PhD, Physics
January 1, 2011 – January 1, 2017
Brigham Young University
Bachelors, Physics and Mathematics
January 1, 2007 – January 1, 2011
National Institute of Science and Technology, Kathmandu Nepal
+2, Physical Sciences
March 1, 2004 – March 1, 2006
JPMorganChase
Applied AIML Lead - VP
May 1, 2025 – Present
New Jersey, United States
CVS Health
Senior Data Scientist
October 1, 2021 – May 1, 2025
Mercy
Data Scientist - Machine Learning
February 1, 2018 – September 1, 2021
Greater St. Louis
Prediction and Control with Function Approximation
Coursera
June 24, 2026 – Present
How to Win a Data Science Competition: Learn from Top Kagglers
Coursera
June 24, 2026 – Present
Clarity Data Model-Resolute Hospital Billing
Epic
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Sample-based Learning Methods
Coursera
June 24, 2026 – Present
IBM Quantum Challenge
IBM
June 24, 2026 – Present
Build Basic Generative Adversarial Networks (GANs)
Coursera
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
IBM Quantum Challenge 2021 Achievement - Advanced
IBM
June 24, 2026 – Present
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Fundamentals of Reinforcement Learning
Coursera
June 24, 2026 – Present
A Complete Reinforcement Learning System (Capstone)
Coursera
June 24, 2026 – Present
Reinforcement Learning Specialization
Coursera
June 24, 2026 – Present
A Crash Course in Causality: Inferring Causal Effects from Observational Data
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career progression from Data Scientist to Applied AIML Lead demonstrates ambition and a drive for growth. The diverse certifications in cutting-edge AI/ML topics suggest a proactive and curious mindset. The experience across different industries (healthcare, finance) indicates adaptability. However, the target role is 'Data Analyst' while the candidate's experience is heavily skewed towards 'Data Scientist' and 'AIML Lead', which might indicate a potential mismatch in role expectations or a desire for a less technical, more analytical role. The lack of specific project details makes it hard to assess collaboration or impact within teams.
Soft Skills & Operational Fit
The candidate's experience in leading AIML initiatives and developing predictive models suggests strong analytical, problem-solving, and potentially leadership skills. The description of work at Mercy indicates a focus on driving operational efficiency and improving patient care, which aligns with a results-oriented approach. However, without specific project details or behavioral assessment data, it is difficult to fully assess soft skills like teamwork, communication, or stress handling.