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Data Scientist with 3+ years deploying ML models using MLflow on Azure Databricks and building FastAPI microservices on Azure Kubernetes Service. Experienced in production-grade predictive solutions using XGBoost classification, ALS collaborative filtering, Prophet time-series forecasting, and automated feature engineering. Skilled in end-to-end MLOps (experiment tracking, model registry, deployment), statistical testing (A/B testing, hypothesis testing), and data-driven model optimization. Strong programming and analytics background using Python, SQL, and modern ML/DL frameworks, along with stakeholder-focused communication and collaboration.…Data Scientist with 3+ years deploying ML models using MLflow on Azure Databricks and building FastAPI microservices on Azure Kubernetes Service. Experienced in production-grade predictive solutions using XGBoost classification, ALS collaborative filtering, Prophet time-series forecasting, and automated feature engineering. Skilled in end-to-end MLOps (experiment tracking, model registry, deployment), statistical testing (A/B testing, hypothesis testing), and data-driven model optimization. Strong programming and analytics background using Python, SQL, and modern ML/DL frameworks, along with stakeholder-focused communication and collaboration.

Meher Venkat

Data Scientist, AI Engineer, Data Analyst, +1

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Data Scientist with 3+ years deploying ML models using MLflow on Azure Databricks and building FastAPI microservices on Azure Kubernetes Service. Experienced in production-grade predictive solutions using XGBoost classification, ALS collaborative filtering, Prophet time-series forecasting, and automated feature engineering. Skilled in end-to-end MLOps (experiment tracking, model registry, deployment), statistical testing (A/B testing, hypothesis testing), and data-driven model optimization. Strong programming and analytics background using Python, SQL, and modern ML/DL frameworks, along with stakeholder-focused communication and collaboration.…Data Scientist with 3+ years deploying ML models using MLflow on Azure Databricks and building FastAPI microservices on Azure Kubernetes Service. Experienced in production-grade predictive solutions using XGBoost classification, ALS collaborative filtering, Prophet time-series forecasting, and automated feature engineering. Skilled in end-to-end MLOps (experiment tracking, model registry, deployment), statistical testing (A/B testing, hypothesis testing), and data-driven model optimization. Strong programming and analytics background using Python, SQL, and modern ML/DL frameworks, along with stakeholder-focused communication and collaboration.

Available to hire

Data Scientist with 3+ years deploying ML models using MLflow on Azure Databricks and building FastAPI microservices on Azure Kubernetes Service. Experienced in production-grade predictive solutions using XGBoost classification, ALS collaborative filtering, Prophet time-series forecasting, and automated feature engineering.

Skilled in end-to-end MLOps (experiment tracking, model registry, deployment), statistical testing (A/B testing, hypothesis testing), and data-driven model optimization. Strong programming and analytics background using Python, SQL, and modern ML/DL frameworks, along with stakeholder-focused communication and collaboration.

Skills

Experience Level

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Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Work Experience

Data Scientist at PNC

January 1, 2025 - Present

Architected an end-to-end MLOps pipeline on Azure Databricks using MLflow (experiment tracking and model registry), deploying an XGBoost predictive model that reduced retail banking customer attrition by 17%. Engineered a credit risk XGBoost classification model served as a FastAPI microservice on Azure Kubernetes Service with Docker, automating 15K+ daily loan approvals at 89% accuracy with sub-second latency. Built K-Means customer segmentation to identify 5 actionable segments improving regional marketing conversion by 14%. Developed call center staffing time-series forecasting using Prophet and statsmodels achieving 92% weekly predictive accuracy and reduced overtime costs. Designed a Python A/B testing framework using statistical hypothesis testing and causal inference across 50K+ users, validating statistically significant feature adoption at 95% confidence.

Data Scientist at Vivma Software Inc

August 1, 2021 - December 1, 2023

Built a product recommendation engine using collaborative filtering (ALS) on 2M+ customer interaction records, increasing e-commerce conversion rate and average order value. Developed price optimization using regression and elasticity modeling across 500+ SKUs to recommend price adjustments contributing to $500K incremental revenue. Implemented automated feature engineering with ANOVA-based selection, reducing dimensionality by 40% and improving churn prediction accuracy by 11%. Designed a CLV prediction model using XGBoost to segment customers into 5 value tiers, enabling data-driven marketing with 15% more budget for high-value retention campaigns. Performed cohort retention analysis and demand forecasting using PostgreSQL feature extraction pipelines, reducing data preparation time by 35% and improving reporting efficiency.