Principal AI Data Scientist

Phoenix, NM - USA

Monthly Salary: Not Disclosed

Posted on: 18 hours ago

Vacancies: 1 Vacancy

Job Summary

Description

As a Principal Data Scientist and AI Developerhere 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).

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 advancedmachine learning modelsLLM-based solutions andagentic AI systemsto solve complex business problems across diverse domains.
Conduct exploratory data analysis statistical assessments and feature engineering on structured semistructured and unstructured datasets.
Build and evaluateGenAI workflowsincluding prompt engineering finetuning RAG pipelines embedding analysis and context optimization.
Develop and validateagentic AI behaviors including reasoning chains tooluse 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 systemscovering 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 AIincluding new model families multimodal approaches vector search innovations and agentic frameworksand assess applicability within the enterprise.
Contribute to reusable AI assets such as feature stores embedding stores evaluation datasets agent toolkits and documentation playbooks.

Qualifications

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 scikitlearn PyTorch or TensorFlow HuggingFace Transformers).
Applied experience developingLLM-based solutions including prompt engineering retrieval-augmented generation (RAG) embeddings and evaluation.
Experience working withDatabricks(Spark Delta Lake Unity Catalog MLflow) for data preparation training and experiment tracking.
Experience withDataikufor 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.

WE VALUE

Bachelors or Masters degree in Computer Science Mathematics Statistics Engineering or a related quantitative discipline.
Experience withagentic 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 chainofthought content safety and task reliability.
Familiarity with vector databases (Databricks Vector Search OpenSearch Pinecone Milvus) and semantic search techniques.
Experience analyzing and preparing multimodal 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 forward the boundaries of applied AI across classic GenAI and agentic approaches.

BENEFITS OF WORKING FOR HONEYWELL

In addition to a competitive salary leading-edge work and developing solutions side-by-side with dedicated experts in their fields Honeywell employees are eligible for a comprehensive benefits package. This package includes employer-subsidized Medical Dental Vision and Life Insurance; Short-Term and Long-Term Disability; 401(k) match Flexible Spending Accounts Health Savings Accounts EAP and Educational Assistance; Parental Leave Paid Time Off (for vacation personal business sick time and parental leave) and 12 Paid Holidays. For more information visit: click here

The application period for the job is estimated to be 40 days from the job posting date; however this may be shortened or extended depending on business needs and the availability of qualified candidates.

#AERO26

Required Experience:

Staff IC

DescriptionAs a Principal Data Scientist and AI Developerhere 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...

Description

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 advancedmachine learning modelsLLM-based solutions andagentic AI systemsto solve complex business problems across diverse domains.
Conduct exploratory data analysis statistical assessments and feature engineering on structured semistructured and unstructured datasets.
Build and evaluateGenAI workflowsincluding prompt engineering finetuning RAG pipelines embedding analysis and context optimization.
Develop and validateagentic AI behaviors including reasoning chains tooluse 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 systemscovering 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 AIincluding new model families multimodal approaches vector search innovations and agentic frameworksand assess applicability within the enterprise.
Contribute to reusable AI assets such as feature stores embedding stores evaluation datasets agent toolkits and documentation playbooks.

Qualifications

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 scikitlearn PyTorch or TensorFlow HuggingFace Transformers).
Applied experience developingLLM-based solutions including prompt engineering retrieval-augmented generation (RAG) embeddings and evaluation.
Experience working withDatabricks(Spark Delta Lake Unity Catalog MLflow) for data preparation training and experiment tracking.
Experience withDataikufor 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.

WE VALUE

Bachelors or Masters degree in Computer Science Mathematics Statistics Engineering or a related quantitative discipline.
Experience withagentic 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 chainofthought content safety and task reliability.
Familiarity with vector databases (Databricks Vector Search OpenSearch Pinecone Milvus) and semantic search techniques.
Experience analyzing and preparing multimodal 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 forward the boundaries of applied AI across classic GenAI and agentic approaches.

BENEFITS OF WORKING FOR HONEYWELL

#AERO26

Required Experience:

Staff IC

Key Skills

Machine Learning
Python
Data Science
AI
R
Research Experience
Sensors
Drug Discovery
Research & Development
Natural Language Processing
Data Analysis Skills
Toxicology Experience

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About Company

Honeywell

Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability ... View more

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