# Pranit Yadav

> AI/ML Engineer | Building Scalable ML Systems | NLP, Fraud Detection, MLOps | AWS, PySpark | 4+ Years Experience

Location: New Bedford, Massachusetts, United States
Profile: https://flows.cv/pranityadav

I am an AI/ML Engineer with over 4 years of experience building and deploying scalable, production-grade machine learning solutions, primarily within financial analytics, fraud detection, and portfolio optimization. Currently, I work at Arrowstreet Capital, where I develop advanced machine learning and NLP models that enhance investment strategies and improve asset performance. My expertise spans the full machine learning lifecycle, including data preprocessing, feature engineering, model development, deployment, and monitoring, with a strong focus on MLOps practices to ensure reliability and scalability. I have hands-on experience with Python, PySpark, TensorFlow, and cloud platforms such as AWS, Azure, and GCP, and have consistently delivered measurable impact, including improving prediction accuracy, reducing processing time, and optimizing model deployment cycles. I am particularly interested in solving complex, real-world problems through data-driven approaches and building intelligent systems that operate at scale. I am currently seeking full-time opportunities as a Machine Learning Engineer or AI Engineer in the United States, where I can contribute to innovative, high-impact projects.

## Work Experience
### AI Engineer @ Arrowstreet Capital, Limited Partnership
Jan 2025 – Present | United States
• Developed and deployed machine learning models using TensorFlow and PyTorch for portfolio optimization, improving predictive accuracy of asset performance by 18% and enhancing investment decision strategies.
• Implemented MLOps pipelines using MLflow and Docker to streamline model versioning and deployment, reducing model release cycles by 35% and improving reproducibility across data science teams.
• Built NLP-based models leveraging transformers for financial text analysis, extracting insights from reports and improving sentiment classification accuracy by 22% for investment research workflows.
• Designed CI/CD pipelines for machine learning workflows, enabling automated testing and deployment, reducing manual intervention by 40% and ensuring consistent model performance across production environments.
• Conducted A/B testing on model-driven investment strategies, validating performance improvements and achieving a 15% uplift in strategy effectiveness compared to traditional baseline approaches.

### AI Engineer @ Capgemini
Jan 2022 – Jan 2024 | India
• Engineered scalable ETL pipelines using PySpark and Apache Spark to process high-volume financial data, reducing data processing time by 40% and improving pipeline reliability for enterprise analytics systems.
• Built and deployed fraud detection models leveraging classification techniques, enhancing transaction monitoring systems and reducing fraudulent activity detection latency by 28% while maintaining high precision and recall balance.
• Designed data pipelines integrated with AWS services including S3 and Lambda, enabling efficient batch processing and improving data availability for downstream analytics and reporting teams by 35%.
• Implemented cloud-based data solutions using Azure Machine Learning and GCP BigQuery, optimizing query performance and
reducing data retrieval time by 25% across multiple financial reporting workflows.
• Developed interactive dashboards using Tableau and Power BI to visualize financial KPIs, improving stakeholder decision-making speed and reducing manual reporting cycles by 30% across client engagements.
• Automated data validation and quality checks within ETL workflows, reducing data inconsistencies by 27% and ensuring compliance with financial data governance standards across multiple enterprise systems.

### ML Engineer @ BrightMind TechWorks
Jan 2020 – Jan 2021 | India
• Cleaned and transformed raw transaction data using Python and SQL to resolve missing values and inconsistencies, improving dataset usability and reducing downstream reporting errors by 18% across internal systems.
• Analyzed customer transaction data using Pandas and NumPy to identify usage patterns, supporting segmentation initiatives that improved targeting strategies and increased campaign response rates by nearly 14% for financial products.
• Built classification models using Scikit-learn for churn prediction, applying feature engineering and cross-validation, improving baseline model performance by 16% and enabling better prioritization of high-risk customer segments.
• Applied anomaly detection methods on transaction records to flag unusual activity, assisting fraud teams in identifying potential risks earlier and reducing manual investigation workload by approximately 10% across selected datasets.
• Developed time series models to track financial trends, supporting monthly planning cycles and improving forecast accuracy by around 11% compared to previously used manual estimation approaches.


## Education
### Master of Science - MS in Data science
University of Massachusetts Dartmouth

### Bachelor of Engineering - BE in Computer Science
Pune University


## Contact & Social
- LinkedIn: https://linkedin.com/in/pranit-yadav-b891211b7

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Source: https://flows.cv/pranityadav
JSON Resume: https://flows.cv/pranityadav/resume.json
Last updated: 2026-04-16