Data Scientist

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 HQ on a Full-Time / 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.

Responsibilities

Key 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.
• Design, develop, and maintain enterprise risk and fraud analytics models focusing on KRIs, risk scoring, anomaly detection, and fraud prevention while leveraging data visualization tools, such as PowerBI.
• 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.

Qualifications

YOU MUST HAVE

• 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, business intelligence /data visualization or data warehouse solutions using technologies such as PowerBI, Snowflake, Databricks, or Spark.
• 3+ years of experience applying data analytics to Enterprise Risk Management, business, risk, audit, compliance, cybersecurity, or fraud-related problems.