Role overview
As a Principal Data Scientist and AI Developer here at Honeywell, you will play a crucial role in designing and implementing advanced data solutions for AI solutions that drive business insights, enhance decision-making processes and empower AI solutions. Your expertise will help in critical AI development activities across all AI modalities (classic, Gen and agentic) and data types (structured and unstructured).
What you'll work on
- Design, develop, and deploy advanced machine learning models , LLM-based solutions , and agentic AI systems to solve complex business problems across diverse domains.
- Conduct exploratory data analysis, statistical assessments, and feature engineering on structured, semi‑structured, and unstructured datasets.
- Build and evaluate GenAI workflows including prompt engineering, fine‑tuning, RAG pipelines, embedding analysis, and context optimization.
- Develop and validate agentic AI behaviors , including reasoning chains, tool‑use strategies, action planning, memory utilization, and safety constraints.
- Partner with Data Engineers, AI Developers, Platform Engineers, and MLOps to bring models and agents into production using Databricks, Dataiku, MLflow, and AWS-native deployment patterns.
- Develop robust evaluation frameworks for ML models, LLMs, and agentic systems—covering accuracy, robustness, hallucination resistance, safety, bias, reliability, and task success rate.
- Implement experiments, compare algorithms, perform ablation studies, and use statistical methods to quantify improvements for both classic ML and LLM-based systems.
- Translate complex AI insights (predictions, feature impacts, agent decisions, retrieval context) into clear business recommendations and decision frameworks.
- Stay current with emerging trends in AI—including new model families, multi‑modal approaches, vector search innovations, and agentic frameworks—and assess applicability within the enterprise.
Contribute to reusable AI assets such as feature stores, embedding stores, evaluation datasets, agent toolkits, and documentation playbooks.
10+ years of experience building, evaluating, and deploying machine learning models in production environments.
Strong proficiency in Python and key ML/AI libraries (pandas, NumPy, scikit‑learn, PyTorch or TensorFlow, HuggingFace Transformers).
Applied experience developing LLM-based solutions , including prompt engineering, retrieval-augmented generation (RAG), embeddings, and evaluation.
Experience working with Databricks (Spark, Delta Lake, Unity Catalog, MLflow) for data preparation, training, and experiment tracking.
Experience with Dataiku for workflow orchestration, data pipelines, and model deployment/use in AI applications.
Hands-on experience with AWS data and AI services such as S3, Lambda, Step Functions, Glue, Bedrock, or SageMaker.
Strong statistical background with experience in hypothesis testing, regression, clustering, classification, and optimization techniques.
Ability to communicate complex findings clearly to technical and non-technical stakeholders.
Proven ability to collaborate in cross-functional agile teams, partnering with engineering, MLOps, and product owners.