About the Job

Join a team recognized for leadership, innovation, and diversity.

As a Lead Decision Scientist/Data Scientist supporting Commercial Excellence at Honeywell Aerospace Technologies, you will play a key role in developing advanced analytics and AI solutions that improve commercial decision-making across pricing, sales, marketing, offering management, and strategy.

Reporting to the Director, Analytics & Insights, you will lead the development of machine learning models, optimization solutions, and decision-support tools that enable commercial teams to improve growth, margin, and productivity. This role works closely with commercial stakeholders, analytics engineers, and data platform teams to translate business problems into scalable analytics and AI solutions.

You will also provide technical leadership and mentorship to other Data Scientists, Data Analysts on the team, helping build advanced analytics capabilities within the Commercial Analytics Lab supporting the Commercial AI roadmap.

You Must Have

• 7+ years of experience in data science, machine learning, advanced analytics, or decision science
• Experience developing and deploying machine learning models in production environments
• Strong programming experience in Python, R, or similar languages
• Experience working with large datasets and cloud data platforms such as Databricks, Snowflake, or Microsoft Fabric
• Experience translating business problems into analytics and machine learning solutions
• Strong analytical, communication, and stakeholder engagement skills

Must be a U.S. Citizen due to contractual requirements.

We Value

• Experience applying machine learning to commercial analytics, pricing, or sales optimization
• Experience building predictive and prescriptive analytics models
• Experience mentoring junior data scientists or analytics professionals
• Experience deploying ML models into scalable production environments
• Familiarity with experimentation frameworks, causal inference, or decision optimization methods
• Experience working in cross-functional business environments

Key Responsibilities

Advanced Analytics & Machine Learning Leadership

• Lead the development of advanced analytics, machine learning, and optimization models supporting 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 model development best practices including feature engineering, model validation, experimentation, and performance monitoring.

Commercial Decision Science & AI Solutions

• Develop AI-powered decision-support tools that enable commercial teams to improve pricing effectiveness, sales productivity, and commercial planning.
• Build predictive models and ML classifiers supporting use cases such as price optimization, demand forecasting, win-rate prediction, and opportunity prioritization.
• Partner with analytics engineers to operationalize models into scalable data products and production solutions.
• Support development of AI-powered commercial copilots and decision intelligence capabilities.

Commercial Analytics Lab Leadership

• Contribute to the development and scaling of the Commercial Analytics Lab, enabling rapid experimentation and deployment of analytics and AI solutions.
• Collaborate with commercial subject matter experts to identify high-impact opportunities for advanced analytics and AI.
• Lead analytics prototyping efforts that demonstrate measurable business value and support commercialization of successful solutions.
• Partner with analytics engineering, enterprise IT and data platform teams to transition successful prototypes into enterprise-grade solutions.

Mentorship & Technical Leadership

• Provide technical mentorship and guidance to an Advanced Data Scientist, supporting their development in machine learning, experimentation, and decision science methods.
• Promote best practices in model development, experimentation design, and analytics reproducibility.
• Foster collaboration between data scientists, analytics engineers, and commercial stakeholders.
• Help build scalable data science capabilities within the commercial analytics organization.

Additional

• Hybrid Work Schedule Note: For the first 90 days, new hires must be prepared to work 100% onsite Monday–Friday.
• Travel: Up to 10%
• Reports To: Director of Analytics & Insights

Benefits of Working for Honeywell

In addition to performance-driven salary, cutting-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; Educational Assistance; Parental Leave; Paid Time Off (vacation, personal business, sick time); and 12 Paid Holidays.

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.