Lead Data Scientist

Honeywell Aerospace•Phoenix, AZ

2d•Hybrid

About The Position

As the Lead Data Scientist supporting Commercial Excellence at Honeywell Aerospace, you will help transform commercial decision-making across commercial and pricing functions through advanced analytics, machine learning, and agentic AI. Reporting to the Senior Manager, Commercial Lab – Advanced Analytics & AI, you will partner with commercial leaders to identify high-value opportunities and develop scalable solutions that deliver business outcomes. You will lead hands-on development of predictive models, optimization algorithms, AI agents, and decision-support tools, from early experimentation through support for production deployment. The role also provides technical leadership and mentorship to Data Scientists and Data Analysts and helps establish next-generation AI capabilities within the Commercial Analytics Lab. The position is based in Phoenix, Arizona (preferred), or Charlotte, North Carolina. It follows a hybrid schedule: the first 90 days are 100% onsite Monday through Friday; afterward, the team is expected in the office at least three days per week, with up to two remote days. Travel is up to 10%.

Requirements

At least 5 years of experience in data science, machine learning, advanced analytics, decision science, or applied AI.
Strong proficiency in Python and experience developing and deploying machine-learning models.
Experience working with cloud data platforms such as Databricks, Google Cloud Platform, Snowflake, or Microsoft Fabric.
Ability to translate ambiguous business challenges into analytical approaches and practical, scalable solutions.
Experience collaborating directly with business stakeholders to define requirements, communicate insights, and deliver measurable business value.
Must be a U.S. Person as defined for compliance with U.S. export control laws and regulations: a U.S. citizen, U.S. permanent resident, person with protected status under asylum or refugee status, or someone able to obtain export authorization.

Nice To Haves

Experience developing AI agents, LLM-powered applications, RAG solutions, or multi-agent workflows using LangGraph, LangChain, or similar technologies.
Experience applying machine learning and AI to real-world business challenges in pricing, sales, or commercial operations.
Strong business acumen, intellectual curiosity, and ability to connect advanced technologies with tangible business outcomes.
Experience rapidly prototyping and iterating on AI solutions in a collaborative, cross-functional environment.
Ability to communicate complex technical concepts clearly and actionably to business leaders and executive stakeholders.

Responsibilities

Lead development of advanced analytics, machine learning, and optimization models for commercial decision-making.
Design and deploy predictive and prescriptive models across pricing, sales performance, forecasting, marketing analytics, and commercial strategy.
Translate complex business problems into scalable analytics solutions and decision frameworks.
Guide best practices for feature engineering, model validation, experimentation, and performance monitoring.
Design, develop, and deploy AI agents and agentic workflows to automate commercial processes and support intelligent decision-making.
Build solutions using large language models, retrieval-augmented generation, and orchestration frameworks to connect AI with enterprise data and business systems.
Develop AI commercial assistants and autonomous or supervised workflows for pricing, sales, proposals, and commercial operations.
Partner with business and technology teams to prototype, evaluate, and operationalize AI solutions with appropriate guardrails, monitoring, and oversight.
Work with commercial and pricing leaders to identify business challenges, assess opportunities, and translate business needs into analytics and AI solutions.
Develop AI decision-support tools to improve pricing effectiveness, sales productivity and outcomes, and commercial planning and strategy.
Build predictive models and machine-learning classifiers for use cases such as price optimization, demand forecasting, win-rate prediction, and opportunity prioritization.
Translate analytical findings into clear business recommendations, actionable insights, and measurable outcomes.
Provide technical mentorship and guidance to an Advanced Data Scientist in machine learning, experimentation, and decision science methods.
Promote best practices in model development, experimentation design, and analytics reproducibility.
Foster collaboration among data scientists, analytics engineers, and commercial stakeholders.
Help build scalable data science capabilities within the commercial analytics organization.