Data Scientist

Role : Data Scientist

About Us :

AppZen is the leader in autonomous spend-to-pay software. Its patented artificial intelligence accurately and efficiently processes information from thousands of data sources so that organizations can better understand enterprise spend at scale to make smarter business decisions. It seamlessly integrates with existing accounts payable, expense, and card workflows to read, understand, and make real-time decisions based on your unique spend profile, leading to faster processing times and fewer instances of fraud or wasteful spend. Global enterprises, including one-third of the Fortune 500, use AppZen’s invoice, expense, and card transaction solutions to replace manual finance processes and accelerate the speed and agility of their businesses. To learn more, visit us at .

About the role :

We are looking for experienced Data Scientists with strong Python expertise to join our growing AI / ML team. You’ll collaborate with a world-class group of machine learning engineers and scientists working on cutting-edge NLP, document understanding, and enterprise automation use cases.

Key Responsibilities

• Design, build, and evaluate models for NLP, document extraction, classification, and generative tasks.
• Develop end-to-end ML pipelines from data preprocessing to model inference and monitoring.
• Work on productionizing models including model packaging, API integration, and deployment using Docker / Kubernetes.
• Analyze model behavior, debug Python code and optimize performance in large-scale environments.
• Translate prototypes into scalable, production-grade ML services, with a focus on reliability and performance.
• Contribute to model and system monitoring, logging, and performance optimization.
• Collaborate with product managers and engineering teams to turn business requirements into ML-driven product features.
• Stay current with research and advancements in transformer-based architectures, LLMs (e.g., GPT, BERT), and generative AI techniques.

Must-Have Qualifications :

Nice-to-Haves :

Ability to choose between heuristic, rule-based, and model-driven solutions pragmatically (e.g., regex vs ML).