Sr Data Scientist & AI DeveloperHoneywell Aerospace Technologies • United States
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Sr Data Scientist & AI Developer
Honeywell Aerospace Technologies
- United States
- United States
À propos
You will report directly to the AI Director and work out of our Phoenix, AZ location on a hybrid schedule. New hires will work onsite Monday‑Friday for the first 90 days.
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, or 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.
Must be a U.S. Citizen due to contractual requirements.
Preferred Qualifications
Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative discipline.
Experience with agentic AI systems, including:
Tool/function calling
Multi‑step reasoning evaluation
Memory and retrieval strategies
Human‑in‑the‑loop review patterns
Safety and guardrail testing
Experience evaluating LLMs for accuracy, hallucination, chain‑of‑thought, content safety, and task reliability.
Familiarity with vector databases (Databricks Vector Search, OpenSearch, Pinecone, Milvus) and semantic search techniques.
Experience analyzing and preparing multi‑modal datasets (text, images, audio, PDFs) for AI solutions.
Knowledge of ML governance, responsible AI principles, bias detection, model explainability, and compliance considerations.
Strong storytelling, data visualization, and dashboarding skills (Tableau or equivalent).
Curiosity, experimentation mindset, and the drive to push the boundaries of applied AI across classic, GenAI, and agentic approaches.
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Compétences linguistiques
- English
Avis aux utilisateurs
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