What are the responsibilities and job description for the Advanced Data Scientist position at Honeywell?
As an Advanced Data Scientist here at Honeywell, you will leverage advanced analytics, artificial intelligence, and machine learning technologies to transform customer engagement, service operations, and sales processes across the enterprise. This role combines deep data science expertise with significant Salesforce platform experience, including Salesforce Agentforce, Einstein AI, Data Cloud, and CRM Analytics.
You will partner with business leaders, product teams, digital transformation organizations, and Salesforce platform teams to design and deploy AI-powered solutions that drive measurable business outcomes. Your work will focus on building intelligent agents, predictive analytics models, conversational AI experiences, customer insight capabilities, and scalable machine learning solutions that enhance operational efficiency and customer experiences.
You will report directly to our Director of R&D, and you will work out of our Hamilton, NJ location on a 3-day in office hybrid work schedule.
KEY RESPONSIBILITIES
- Design, develop, and deploy advanced machine learning and AI solutions that address complex business challenges.
- Lead the development of intelligent agent solutions leveraging Salesforce Agentforce and Einstein AI capabilities.
- Build predictive models for customer behavior, lead scoring, opportunity forecasting, churn prediction, and service optimization.
- Partner with CRM, sales, service, and marketing teams to define AI use cases, success criteria, and measurement frameworks.
- Develop and operationalize generative AI solutions using large language models, retrieval-augmented generation, and enterprise AI architectures.
- Design data pipelines and feature engineering processes utilizing Salesforce Data Cloud and enterprise data platforms.
- Develop conversational AI experiences and autonomous agent workflows integrated with the Salesforce ecosystem.
- Evaluate emerging AI and Salesforce technologies and recommend enterprise adoption strategies.
- Establish model governance, monitoring, explainability, security, and responsible AI practices.
- Collaborate with software engineers, product managers, architects, and business stakeholders to deliver production-ready solutions.
- Translate complex analytical findings into clear, actionable recommendations for business and executive stakeholders.
- Mentor developers and contribute to technical standards and reusable best practices across the organization.