As a Data Scientist II at Honeywell Technology solutions in the life sciences domain, you will play a pivotal role in leveraging data and cutting-edge Generative AI techniques to drive insights, develop innovative models, and accelerate digital transformation. You will work on building advance OCR/CV /GenAI based models and deliver end-to-end data and model pipelines, with a special emphasis on deploying Generative AI solutions. Your expertise in cloud platforms (AWS, Azure) and ML Ops will be crucial for optimizing and scaling these advanced models. This is an exciting opportunity to shape the future of intelligent building systems using the latest advancements in AI.

You Must Have

• A bachelor’s degree in computer science, Software Engineering, or a related field.
• 3 puls years of experience in software development with strong proficiency in Python and .NET.
• Hands‑on experience designing and developing RESTful APIs and backend microservices.
• Strong understanding of distributed systems concepts.
• Experience with React for UI development.
• Good proficiency in SQL with experience in PostgreSQL and/or SQL Server.
• Experience building data engineering pipelines using tools such as Databricks.
• Experience working with cloud systems (Azure/AWS/GCP).

We Value

• Strong understanding of distributed system design patterns.
• Knowledge of Click House DB is a plus.
• Experience with Databricks, Delta Lake, Spark optimization.
• Familiarity with DevOps and CI/CD pipelines.
• Experience working in agile development environments.
• Strong debugging and problem‑solving skills.
• Ability to communicate complex technical concepts clearly.
• Passion for building reliable, scalable systems.

Key Responsibilities

• Design, develop, and maintain scalable backend services and distributed system components using Python and .NET.
• Build and optimize RESTful APIs to support critical applications and high‑traffic system interactions.
• Develop and maintain frontend components using React, ensuring seamless integration with backend systems.
• Applying and/or implementing Machine Learning (ML) or other Artificial Intelligence (AI) techniques to solve business problems and/or derive actionable business insights
• Exposure in creating data mining architectures/models/protocols, statistical reporting, data analysis methodologies to identify trends/patterns.
• Work with SQL Server and PostgreSQL to design efficient schemas, write optimized queries, and manage data workflows.
• Develop end‑to‑end data engineering pipelines, including ingestion, transformation, orchestration, and monitoring.
• Leverage Databricks (Spark, Delta Lake) to build scalable data processing and analytical pipelines.
• Implement and maintain distributed computing and messaging patterns for high‑availability systems.
• Collaborate with cross‑functional engineering, data, and product teams to deliver integrated, high‑quality solutions.
• Participate in code reviews, contribute to CI/CD pipelines, and adopt engineering best practices.
• Troubleshoot system bottlenecks, optimize performance, and ensure operational excellence.