Available to hire
I am an experienced and innovative Data Science and Machine Learning leader with a proven track record of building and scaling AI solutions across telecom, media, healthcare, and life sciences. I specialize in developing and deploying advanced models in natural language processing, large language models, predictive analytics, and MLOps, leveraging cloud platforms such as Azure, AWS, and GCP.
Passionate about applying ethical, explainable AI practices, I have hands-on expertise architecting end-to-end ML pipelines and leading cross-functional teams to deliver production-grade systems that drive measurable business impact. I thrive on transforming complex unstructured data into actionable insights, optimizing customer experiences, automating workflows, and generating multimillion-dollar savings while aligning ML strategy with broader organizational goals.
Skills
Experience Level
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Work Experience
Director of Data Science & ML at Warner Bros. Discovery
April 1, 2022 - PresentLed the enterprise-scale machine learning and AI systems strategy focusing on viewer engagement, content discovery, and streaming optimization across global platforms like Max and Discovery+. Architected a real-time content recommendation engine using deep learning, reinforcement learning, and contextual bandits to improve user engagement and content relevance by over 30%. Designed and deployed large language model-powered personalization frameworks integrating transformers such as BERT and GPT for content ranking and smart search experiences. Directed behavioral segmentation models using unsupervised learning tools to enable hyper-targeted marketing and campaign optimization. Spearheaded NLP pipelines for mining unstructured data using HuggingFace, spaCy, and LangChain to generate actionable insights. Modernized data science infrastructure with cloud-native ML platforms, Kubernetes, Docker, and Terraform automation. Implemented robust MLOps pipelines with CI/CD, automated retraining,
Principal Data Scientist at AT&T
January 1, 2017 - PresentDesigned and led scalable machine learning platforms built on complex financial data to create predictive systems addressing business-critical problems across finance, customer service, and operations. Managed and mentored a team designing advanced ML and NLP solutions using transformer models and deep learning tailored for telecom-specific use cases. Built automated pipelines for summarizing customer-agent call interactions with LLMs like T5 and BART, enhancing documentation, intent extraction, and customer experience analysis. Simplified claim management workflows through predictive analytics, resulting in potential $20M savings from reduced settlement costs. Developed document summarization systems using LangChain and Retrieval-Augmented Generation to efficiently extract high-value information. Applied NLP to call transcripts improving promotional spend strategy and reducing waste by over $7M. Created classification models to automate complex telecom operations, improving service ti
Machine Learning Scientist at Humana
January 1, 2017 - August 22, 2025Contributed to predictive modeling solutions aimed at improving healthcare outcomes, resource allocation, and patient risk stratification within the clinical analytics division. Built and validated supervised learning models such as logistic regression, random forests, and XGBoost to predict hospital readmission risk and identify high-risk patient populations. Collaborated with data engineering and clinical informatics teams to preprocess large-scale EMR and claims datasets ensuring data quality, feature extraction, and HIPAA compliance. Applied NLP to extract structured information from physician notes and patient history, improving model accuracy for chronic disease prediction. Developed custom scoring pipelines and integrated performance monitoring dashboards using Python, SQL, and Tableau to aid clinical decision-making.
Principal Machine Learning Scientist at Zoetis
October 1, 2016 - August 22, 2025Directed design and deployment of advanced ML solutions to accelerate diagnostics, treatment response prediction, and real-time disease surveillance in veterinary and animal health domains. Built and optimized predictive models using supervised, unsupervised, and time-series techniques (ARIMA, LSTM) to forecast disease progression, drug effectiveness, and epidemiological trends. Developed deep learning-based computer vision systems employing CNNs like ResNet and VGG for automated detection of parasitic infections and musculoskeletal disorders from imaging data, enhancing diagnostic throughput by over 40%. Architected scalable bioinformatics pipelines for processing genetic, microbial, and biomarker data supporting research in genomics, drug efficacy, and adverse reaction prediction. Collaborated closely with veterinary scientists and biostatisticians to align ML approaches with clinical trial designs and regulatory requirements. Implemented cloud-based infrastructure for model developm
Principal Scientist – AI & Data Science at Pfizer
May 1, 2013 - August 22, 2025Contributed to Pfizer’s Computational Sciences division as a lead scientist, applying machine learning, bioinformatics, and statistical modeling to accelerate drug discovery and preclinical research across oncology, infectious diseases, and metabolic disorders. Built and validated predictive models using regression, decision trees, support vector machines, and ensemble methods on large-scale omics datasets to support biomarker discovery and target identification. Led dose-response modeling, toxicology prediction, and clinical outcomes simulations reducing time-to-decision for compound prioritization and advancing high-potential molecules to clinical trials. Designed and deployed data integration pipelines unifying structured and unstructured sources (wet lab results, imaging, chemical assays, molecular databases) for scalable and reproducible analysis. Applied unsupervised learning, clustering, and dimensionality reduction (e.g., PCA, t-SNE) to identify latent variables and biologica
Research Assistant at Kansas State University
December 1, 2006 - August 22, 2025Conducted extensive research in computational biology and biostatistics focusing on experimental design, multivariate data analysis, and pattern recognition in biological systems. Designed and executed statistical models to study gene expression profiles and metabolic pathways using R, SAS, and MATLAB for hypothesis testing, clustering, and regression analysis. Collaborated with interdisciplinary teams of biologists, statisticians, and computer scientists on federally funded research projects contributing to publications and conference presentations in systems biology and veterinary medicine. Developed automated data preprocessing pipelines to clean, transform, and visualize large-scale experimental datasets enabling reproducible and efficient downstream analysis. Created and maintained custom scripts and tools for laboratory data analysis including time-series modeling, variance analysis, and PCA for dimensionality reduction. Played a key role in interpreting experimental results and
Director of Data Science & ML at Warner Bros. Discovery
April 1, 2022 - PresentLed the end-to-end strategy, design, and deployment of enterprise-scale machine learning and AI systems focusing on viewer engagement, content discovery, and streaming optimization across global platforms like Max and Discovery+. Architected a real-time content recommendation engine using deep learning, reinforcement learning, and contextual bandits, improving user engagement and content relevance by over 30%. Designed and deployed LLM-powered personalization frameworks using transformer models (BERT, GPT) for content ranking and smart search experiences. Directed development of behavioral segmentation models leveraging unsupervised learning, K-means clustering, and autoencoders to enable hyper-targeted marketing and improve campaign KPI performance. Spearheaded NLP pipelines for mining unstructured data like user reviews and support tickets using HuggingFace, spaCy, and LangChain. Modernized company data science infrastructure by migrating to cloud-native ML platforms AWS SageMaker an
Principal Data Scientist at AT&T
January 1, 2017 - PresentDesigned and led the development of scalable machine learning platforms built on complex financial data, enabling predictive systems that addressed business-critical problems across finance, customer service, and operations. Managed and mentored a team of data scientists and engineers guiding design of advanced ML and NLP solutions using transformer models, deep learning, and classical algorithms tailored for telecom-specific use cases. Built an automated pipeline for summarizing customer-agent expert call interactions using LLMs like T5 and BART, significantly improving internal documentation and customer experience analysis. Simplified and optimized the claim management process for subrogation workflows using a blend of machine learning, deep learning, and predictive analytics, resulting in potential savings of $20M through reduced settlement costs. Developed document summarization systems using LangChain framework and Retrieval-Augmented Generation architecture to efficiently extrac
Machine Learning Scientist at Humana
January 1, 2017 - August 22, 2025Contributed to the development of predictive modeling solutions aimed at improving healthcare outcomes, resource allocation, and patient risk stratification within Humana's clinical analytics division. Built and validated supervised learning models using logistic regression, random forests, and gradient boosting (XGBoost) to predict hospital readmission risk and identify high-risk patient populations based on clinical, claims, and demographic data. Collaborated with data engineering and clinical informatics teams to preprocess large-scale EMR and claims datasets ensuring data quality, feature extraction, and regulatory compliance (e.g., HIPAA). Applied NLP techniques to extract structured information from physician notes and patient history, enabling better representation of comorbidities and improving model accuracy in chronic disease prediction use cases. Developed custom scoring pipelines for model deployment and integrated performance monitoring dashboards using Python, SQL, and Ta
Principal Machine Learning Scientist at Zoetis
October 1, 2016 - August 22, 2025Directed the design and deployment of advanced machine learning solutions to accelerate diagnostics, treatment response prediction, and real-time disease surveillance in veterinary and animal health domains. Built and optimized predictive models using supervised, unsupervised, and time-series techniques (ARIMA, LSTM) to forecast disease progression, drug effectiveness, and epidemiological trends across diverse veterinary datasets. Developed deep learning-based computer vision systems using CNNs (ResNet, VGG) for automated detection of parasitic infections, musculoskeletal disorders, and soft tissue anomalies from imaging data, enhancing diagnostic throughput by over 40%. Architected scalable bioinformatics pipelines for processing genetic, microbial, and biomarker data using Biopython, BLAST, R, and Python supporting research in genomics, drug efficacy, and adverse reaction prediction. Collaborated with veterinary scientists and biostatisticians to align ML approaches with clinical tri
Principal Scientist – AI & Data Science at Pfizer
May 1, 2013 - August 22, 2025Contributed to Pfizer’s Computational Sciences division as a lead scientist, applying machine learning, bioinformatics, and statistical modeling to accelerate drug discovery and preclinical research across oncology, infectious diseases, and metabolic disorders. Built and validated predictive models using regression, decision trees, support vector machines (SVMs), and ensemble methods (e.g., Gradient Boosting) on large-scale omics datasets (genomics, transcriptomics, proteomics) to support biomarker discovery and target identification. Led efforts in dose-response modeling, toxicology prediction, and clinical outcomes simulation significantly reducing time-to-decision for compound prioritization and advancing high-potential molecules to clinical trials. Designed and deployed data integration pipelines unifying structured and unstructured sources (wet lab results, imaging, chemical assays, and molecular databases) to enable reproducible, scalable analysis across research cohorts. Appli
Research Assistant at Kansas State University
December 1, 2006 - August 22, 2025Conducted extensive research in computational biology and biostatistics, focusing on experimental design, multivariate data analysis, and pattern recognition in biological systems. Designed and executed statistical models to study gene expression profiles and metabolic pathways, leveraging R, SAS, and MATLAB for hypothesis testing, clustering, and regression analysis. Collaborated with interdisciplinary teams of biologists, statisticians, and computer scientists on federally funded research projects, contributing to publications and conference presentations in systems biology and veterinary medicine. Developed automated data preprocessing pipelines to clean, transform, and visualize large-scale experimental datasets, enabling reproducible and efficient downstream analysis. Created and maintained custom scripts and tools for laboratory data analysis including time-series modeling, variance analysis, and PCA for dimensionality reduction. Played a key role in interpreting experimental res
Director of Data Science & ML at Warner Bros. Discovery
April 1, 2022 - PresentLed the end-to-end strategy, design, and deployment of enterprise-scale machine learning and AI systems focused on viewer engagement, content discovery, and streaming optimization across global platforms including Max and Discovery+. Architected a real-time content recommendation engine using deep learning, reinforcement learning, and contextual bandits to improve content relevance and user engagement by over 30%. Designed and deployed LLM-powered personalization frameworks integrating transformer models like BERT and GPT, directed the development of behavioral segmentation models with unsupervised learning, and spearheaded NLP pipelines for mining unstructured data such as user reviews and support tickets. Modernized data science infrastructure by migrating to cloud-native ML platforms (AWS SageMaker, GCP Vertex AI) with Kubernetes and Terraform. Implemented robust MLOps pipelines featuring CI/CD, model versioning, drift detection, and real-time monitoring. Championed responsible AI p
Principal Data Scientist at AT&T
January 1, 2017 - PresentDesigned and led the development of scalable machine learning platforms processing complex financial data to create predictive systems solving business-critical problems across finance, customer service, and operations. Managed and mentored a team, guiding advanced ML and NLP solution design using transformer models, deep learning, and classical algorithms tailored for telecom-specific use cases. Built automated pipelines summarizing customer-agent call interactions using LLMs like T5 and BART, improving documentation and customer experience analysis. Simplified and optimized claims management workflows via machine learning, deep learning, and predictive analytics, generating potential $20M savings. Developed document summarization systems using LangChain and retrieval-augmented generation (RAG) architectures for unstructured call records and support logs. Automated analysis of offer and gift card systems by applying NLP to call transcripts, achieving smarter promotional strategies and
Machine Learning Scientist at Humana
January 1, 2017 - August 22, 2025Contributed to the development of predictive modeling solutions aimed at improving healthcare outcomes, resource allocation, and patient risk stratification within Humana's clinical analytics division. Built and validated supervised learning models using logistic regression, random forests, and gradient boosting (XGBoost) to predict hospital readmission risk and identify high-risk patient populations based on clinical, claims, and demographic data. Collaborated with data engineering and clinical informatics teams to preprocess large-scale EMR and claims datasets, ensuring data quality, feature extraction, and HIPAA regulatory compliance. Applied NLP techniques to extract structured information from physician notes and patient history, enhancing comorbidity representation and improving model accuracy in chronic disease prediction use cases. Developed custom scoring pipelines for model deployment and integrated performance monitoring dashboards using Python, SQL, and Tableau, aiding clin
Principal Machine Learning Scientist at Zoetis
October 1, 2016 - August 22, 2025Directed the design and deployment of advanced machine learning solutions for accelerated diagnostics, treatment response prediction, and real-time disease surveillance in the veterinary and animal health domain. Built and optimized predictive models using supervised, unsupervised, and time-series techniques (ARIMA, LSTM) to forecast disease progression, drug effectiveness, and epidemiological trends across diverse veterinary datasets. Developed deep learning-based computer vision systems with CNNs (ResNet, VGG) for automated detection of parasitic infections, musculoskeletal disorders, and soft tissue anomalies from imaging data, enhancing diagnostic throughput by over 40%. Architected scalable bioinformatics pipelines processing genetic, microbial, and biomarker data leveraging Biopython, BLAST, R, and Python to support research in genomics, drug efficacy, and adverse reaction prediction. Collaborated with veterinary scientists and biostatisticians to align ML approaches with clinica
Principal Scientist – AI & Data Science at Pfizer
May 1, 2013 - August 22, 2025Contributed to Pfizer’s Computational Sciences division as a lead scientist, applying machine learning, bioinformatics, and statistical modeling to accelerate drug discovery and preclinical research across oncology, infectious diseases, and metabolic disorders. Built and validated predictive models using regression, decision trees, support vector machines, and ensemble methods (Gradient Boosting) on large-scale omics datasets (genomics, transcriptomics, proteomics) to support biomarker discovery and target identification. Led dose-response modeling, toxicology prediction, and clinical outcomes simulation, significantly reducing time-to-decision for compound prioritization and advancing high-potential molecules to clinical trials. Designed and deployed data integration pipelines unifying structured and unstructured sources (wet lab results, imaging, chemical assays, molecular databases) enabling reproducible, scalable analysis across research cohorts. Applied unsupervised learning, cl
Research Assistant at Kansas State University
December 1, 2006 - August 22, 2025Conducted extensive research in computational biology and biostatistics focusing on experimental design, multivariate data analysis, and pattern recognition in biological systems. Designed and executed statistical models to study gene expression profiles and metabolic pathways using R, SAS, and MATLAB for hypothesis testing, clustering, and regression analysis. Collaborated with interdisciplinary teams of biologists, statisticians, and computer scientists on federally funded research projects, contributing to publications and conference presentations in systems biology and veterinary medicine. Developed automated data preprocessing pipelines to clean, transform, and visualize large-scale experimental datasets enabling reproducible and efficient downstream analysis. Created and maintained custom scripts and tools for laboratory data analysis including time-series modeling, variance analysis, and PCA for dimensionality reduction. Played a key role in interpreting experimental results and
Education
Master of Science (M.S.) at Kansas State University
February 1, 2002 - December 1, 2006Bachelor of Technology (B.Tech) at SNIST, Jawaharlal Nehru Technological University
August 1, 1997 - May 1, 2001Master of Science (M.S.) at Kansas State University
February 1, 2002 - December 1, 2006Bachelor of Technology (B.Tech) at Jawaharlal Nehru Technological University
August 1, 1997 - May 1, 2001Master of Science (M.S.) at Kansas State University
February 1, 2002 - December 1, 2006Bachelor of Technology (B.Tech) at SNIST, Jawaharlal Nehru Technological University
August 1, 1997 - May 1, 2001Qualifications
Industry Experience
Telecommunications, Media & Entertainment, Healthcare, Life Sciences, Financial Services
Skills
Experience Level
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
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