Data Scientist II

Honeywell•Charlotte, NC

13d•Hybrid

About The Position

As a Data Scientist II here at Honeywell, you will leverage your expertise in audit analytics, data engineering, risk and fraud analytics, predictive modeling, and advanced AI technologies to drive impactful data science initiatives aligned with regulatory compliance and business objectives. You will report directly to the Director of IT and Cybersecurity Audit, and you’ll work out of our Charlotte, NC HQ on an in office for first 90 days then Hybrid work schedule. In this role, you will impact the development and deployment of advanced analytics and machine learning models that support audit, risk management, cybersecurity and fraud detection efforts, while ensuring compliance with industry regulations and enhancing operational efficiency.

Requirements

5+ years of experience in data science, audit analytics, risk analytics, fraud analytics, or a related field.
3+ years of experience developing and implementing predictive or machine learning models, including classification, regression, anomaly detection, or risk scoring.
3+ years of experience developing data pipelines, ETL processes, or data warehouse solutions using technologies such as Snowflake, Databricks, or Spark.
3+ years of experience applying data analytics to business, risk, audit, compliance, cybersecurity, or fraud-related problems.

Nice To Haves

Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, or a related field.
Experience applying analytics to audit, risk management, cybersecurity, fraud detection, or regulatory compliance use cases.
Experience with audit analytics aligned to frameworks such as SOX, GDPR, NIST, or PCI DSS.
Experience with explainable AI techniques such as SHAP or LIME.
Experience monitoring model performance, detecting model drift, and retraining machine learning models.
Experience implementing CI/CD pipelines for analytics or machine learning workflows.
Experience with generative AI, agentic AI, large language models (LLMs), natural language processing (NLP), or AI-enabled data summarization.
Experience using security and cybersecurity tools such as Splunk, Microsoft Defender, SentinelOne, or Qualys.
Experience with cloud platforms and machine learning lifecycle management.
Experience working in a regulated industry with compliance requirements.
Experience managing projects and communicating with senior stakeholders.
Strong analytical skills with the ability to translate complex data into actionable insights.
Ability to collaborate effectively across technical and business teams in a fast-paced environment.
Intrapreneurial growth mindset—demonstrated ability to proactively identify opportunities, challenge existing approaches, experiment responsibly, learn from feedback, and take ownership of innovative solutions that create measurable business value.

Responsibilities

Lead audit analytics initiatives aligned with SOX, GDPR, NIST, and PCI DSS frameworks, including scoping, probabilistic modeling, testing, and reporting.
Design, develop, and maintain robust ETL pipelines and data warehouses using Snowflake and Databricks.
Develop and implement risk and fraud analytics models focusing on KRIs, risk scoring, anomaly detection, and fraud prevention.
Build and optimize predictive models for classification and regression tasks.
Utilize SHAP and LIME techniques to interpret and explain model predictions.
Monitor model performance, detect drift, and retrain models as necessary to maintain accuracy.
Leverage Spark for large-scale data processing and analytics.
Implement CI/CD pipelines for analytics and machine learning workflows to ensure seamless deployment and integration.
Utilize security tools such as Splunk, Defender, SentinelOne, and Qualys to support data security and compliance.
Apply generative and agentic AI technologies for audit use cases, integrating platforms like ServiceNow, Azure AI, large language models (LLMs), and natural language processing (NLP) for data summarization and insights.