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# Advanced Data Scientist - Salesforce

- **Company:** Honeywell International Inc.
- **Location:** Trenton, NJ, United States
- **Experience:** Experienced
- **Salary:** $124,000.0 - $154,500.0
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Information Systems, Data Integration, Python (Programming Language), Machine Learning, Cloud Services, Tensorflow, Salesforce.Com, Search Technologies, Enterprise Data Management, Google Cloud, Feature Engineering, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Deep Learning, Generative AI, Salesforce Sales Cloud, Information Technology, Machine Learning Operations, Data Pipelines
- **Published:** October 23, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3466425236&tx=CT3533TTI&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Bachelor's degree from an accredited institution in Computer Science, Data Science, Statistics, Mathematics, Engineering, Information Systems, or a related quantitative field.
 * 5+ years of experience in data science, machine learning, artificial intelligence, and advanced analytics, including developing predictive models using Python and modern machine learning frameworks.
 * 3+ years of experience with Salesforce technologies, including Agentforce, Einstein AI, Data Cloud, Service Cloud, and Sales Cloud, delivering AI-driven business solutions.
 * 3+ years of experience applying statistical modeling, predictive analytics, experimentation, and large-scale data analysis while collaborating with stakeholders to drive data-driven business decisions.

WE VALUE

 * Master's degree or PhD in a quantitative discipline.
 * Salesforce Agentforce Specialist, Salesforce AI Specialist, or related Salesforce certifications.
 * Expertise with generative AI, large language models, prompt engineering, and enterprise AI applications.
 * Experience implementing AI-powered customer service or digital sales transformation initiatives.
 * Knowledge of cloud platforms such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform.
 * Experience with MLOps, model deployment, observability, monitoring, and lifecycle management.
 * Familiarity with vector databases, semantic search, and retrieval architectures.
 * Experience working in large enterprise environments with complex data and integration ecosystems.
 * Strong business acumen and the ability to connect AI investments to measurable business outcomes.
 * Agile Development methodologies., Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. Person, which is defined as, a U.S. citizen, a U.S. permanent resident, or have protected status in the U.S. under asylum or refugee status or have the ability to obtain an export authorization.

## Description

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., * 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.