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 www.appzen.com.

About the role:

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:

• 35 years of professional experience in Python, with strong debugging, profiling, and performance optimization skills.
• Solid understanding of python data structures, algorithms, and software engineering best practices in ML development.
• Hands-on experience with NLP and modern ML frameworks like PyTorch, TensorFlow, or HuggingFace Transformers.
• Applied experience with transformer models, LLMs, or generative AI in real-world scenarios.
• Experience with model evaluation, including designing meaningful metrics, tracking model drift, and optimizing performance in production.
• Ability to manage multiple priorities in a fast-paced and collaborative environment.
• B.E./B.Tech or higher in Computer Science, Engineering, or a related technical field.

Nice-to-Haves:

• Experience building and deploying containerized ML services with Docker and CI/CD pipelines.
• Skilled in designing and consuming RESTful Python APIs (e.g., FastAPI, Flask).
• Experience with cloud services, particularly AWS (S3, SQS, etc.).
• Familiarity with databases such as PostgreSQL and Redis
• Strong grasp of classical ML algorithms such as Logistic Regression, Random Forests, and XGBoost.

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