Sr Data Scientist & AI Developer
Honeywell Aerospace US LLC
- United States
- United States
À propos
As a
Sr 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). You will report directly to our AI Director and you’ll work out of our Phoenix, AZ location on a Hybrid work schedule. Note: New Hires will work onsite M-F for the first 90 days. In this role, you will impact the organization by leveraging your technical skills to develop innovative software solutions that support strategic initiatives and improve operational efficiency. Responsibilities
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. Qualifications
Bachelor’s degree from an accredited institution in a technical discipline such as science, technology, engineering, mathematics. 4–7 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. US CITIZEN REQUIREMENT Must be a US Citizen due to contractual requirements Note
Other content such as benefits, postings, and corporate information has been omitted to keep the job description focused on responsibilities, qualifications, and essential role context.
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Compétences linguistiques
- English
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