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AI/ML Engineer with 10+ years of experience in data science, machine learning, advanced analytics, and production data science engineering across financial services, risk, and customer analytics. Hands-on expertise building large-scale data pipelines and ML workflows using Python, SQL, PySpark, Spark MLlib, and cloud platforms. Experienced in end-to-end feature engineering, model development (classification, regression, clustering, anomaly/fraud and risk modeling), model evaluation and tuning, and production readiness with MLOps practices such as MLflow, drift monitoring, and workflow orchestration. Strong communication skills to translate ambiguous business problems into measurable experiments and decision-ready insights for stakeholders.…AI/ML Engineer with 10+ years of experience in data science, machine learning, advanced analytics, and production data science engineering across financial services, risk, and customer analytics. Hands-on expertise building large-scale data pipelines and ML workflows using Python, SQL, PySpark, Spark MLlib, and cloud platforms. Experienced in end-to-end feature engineering, model development (classification, regression, clustering, anomaly/fraud and risk modeling), model evaluation and tuning, and production readiness with MLOps practices such as MLflow, drift monitoring, and workflow orchestration. Strong communication skills to translate ambiguous business problems into measurable experiments and decision-ready insights for stakeholders.

Anusha Tatapudi

AI Engineer, Data Scientist, Data Engineer, +2

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AI/ML Engineer with 10+ years of experience in data science, machine learning, advanced analytics, and production data science engineering across financial services, risk, and customer analytics. Hands-on expertise building large-scale data pipelines and ML workflows using Python, SQL, PySpark, Spark MLlib, and cloud platforms. Experienced in end-to-end feature engineering, model development (classification, regression, clustering, anomaly/fraud and risk modeling), model evaluation and tuning, and production readiness with MLOps practices such as MLflow, drift monitoring, and workflow orchestration. Strong communication skills to translate ambiguous business problems into measurable experiments and decision-ready insights for stakeholders.…AI/ML Engineer with 10+ years of experience in data science, machine learning, advanced analytics, and production data science engineering across financial services, risk, and customer analytics. Hands-on expertise building large-scale data pipelines and ML workflows using Python, SQL, PySpark, Spark MLlib, and cloud platforms. Experienced in end-to-end feature engineering, model development (classification, regression, clustering, anomaly/fraud and risk modeling), model evaluation and tuning, and production readiness with MLOps practices such as MLflow, drift monitoring, and workflow orchestration. Strong communication skills to translate ambiguous business problems into measurable experiments and decision-ready insights for stakeholders.

Available to hire

AI/ML Engineer with 10+ years of experience in data science, machine learning, advanced analytics, and production data science engineering across financial services, risk, and customer analytics. Hands-on expertise building large-scale data pipelines and ML workflows using Python, SQL, PySpark, Spark MLlib, and cloud platforms.

Experienced in end-to-end feature engineering, model development (classification, regression, clustering, anomaly/fraud and risk modeling), model evaluation and tuning, and production readiness with MLOps practices such as MLflow, drift monitoring, and workflow orchestration. Strong communication skills to translate ambiguous business problems into measurable experiments and decision-ready insights for stakeholders.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Beginner

Language

English

Advanced

Work Experience

Sr. AI/ML Engineer at Charles Schwab

June 1, 2025 - Present

Built production data science pipelines for financial analytics and risk workflows using Python, SQL, and PySpark. Explored and aggregated transaction, customer, product, and operational datasets to identify anomalies and data quality issues affecting model performance. Led end-to-end feature engineering with stakeholder collaboration, validating signal quality and down-selecting predictors to improve model stability. Developed fraud and risk model prototypes using classification, anomaly detection, gradient boosting, and statistical methods, and delivered model evaluation artifacts (ROC-AUC, precision/recall, lift, and error analysis). Tuned complex SQL queries using CTEs, window functions, and aggregations for performance at scale. Implemented data validation rules and monitoring for drift, scoring exceptions, batch failures, and runtime reliability. Created Tableau/Power BI/Looker dashboards to communicate model outcomes and data quality metrics to non-technical stakeholders while f

ML Engineer at Fractal

September 1, 2022 - December 31, 2024

Engineered end-to-end data science workflows including ingestion, exploration, feature engineering, model training, evaluation, deployment support, and monitoring for enterprise analytics. Established reusable experimentation and MLOps practices with MLflow and GitHub for experiment tracking, artifact/version management, and reproducible delivery. Designed Apache Airflow DAGs for batch data preparation, feature generation, model scoring, and quality checks. Built scalable PySpark/Databricks workflows for terabyte-scale structured and semi-structured datasets. Developed machine learning solutions for classification, regression, clustering, recommendation-style use cases, and fraud/anomaly/risk scoring using scikit-learn, Spark MLlib, TensorFlow, PyTorch, and XGBoost. Exposed scoring and validation outputs via secure Python APIs (Flask/FastAPI). Supported deployment preparation with AWS services (S3, EC2, Lambda, SageMaker), containerized services with Docker/Kubernetes, and maintained p

Data Scientist at Pinnacle

October 1, 2019 - August 31, 2022

Designed and implemented machine learning and advanced analytics solutions including classification, regression, clustering, and forecasting for decision support. Performed exploratory data analysis and visualization to uncover trends, anomalies, correlations, and hidden patterns. Conducted feature engineering using statistical methods and domain knowledge with rigorous data quality checks. Built supervised models (Logistic Regression, Random Forest, Gradient Boosting, XGBoost) for forecasting and risk-style decisioning; applied unsupervised methods for customer segmentation and pattern discovery. Evaluated models using precision/recall, F1, ROC-AUC, lift, and error analysis; executed hyperparameter tuning with grid/random search and cross-validation. Used SQL with PostgreSQL and Amazon Redshift to extract and validate large datasets. Deployed models as RESTful APIs using Flask and supported experimentation/deployment workflows with AWS. Created Tableau/Power BI dashboards and explaina

Data Analyst at S&P Global

June 1, 2015 - September 30, 2019

Cleaned, standardized, validated, and transformed large business datasets using Python, Pandas, NumPy, SQL, and Excel for reporting and analytics. Conducted statistical analysis, regression, trend analysis, and hypothesis testing to generate actionable recommendations. Optimized SQL performance using joins, CTEs, aggregations, and validation checks for high-volume reporting. Performed exploratory analysis to identify data gaps, outliers, and inconsistencies. Automated recurring reporting and data preparation using Python scripts and Excel VBA. Supported ETL and data integration workflows for centralized reporting environments. Developed Tableau/Power BI dashboards to monitor KPIs and operational trends, and authored complex SQL scripts using window functions for reporting and ad hoc analysis. Managed structured datasets and data accuracy using Amazon Redshift and Microsoft SQL Server while supporting governance via documentation of methods and SQL logic.