Senior Data Scientist, Vice President - Corporate Functions Technology | Quincy, MA, USA

Senior Data Scientist, Vice President - Corporate Functions Technology

{"description": " Who We Are Looking For

We are seeking a Senior Data Scientist, Vice President to design and deliver advanced analytics and machine learning solutions supporting our Internal Audit Functions . In this hands-on role, you will apply statistical modeling, machine learning, and responsible AI to drive risk-based audit planning, continuous risk monitoring, and actionable insights in a regulated enterprise environment.

This is a senior individual-contributor role with end-to-end accountability for model development, governance, and delivery. You will serve as a senior technical leader and subject-matter expert, partnering closely with audit, data engineering, and architecture teams to embed analytics and AI into audit workflows in a way that enhances auditor effectiveness and meets enterprise and regulatory standards.

What You Will Be Responsible For

Model Development

• Design, build, and refine statistical and machine learning models to identify risk patterns such as trends, clusters, outliers, and anomalies.

• Generate ranked risk signals and insights to support auditor review, prioritization, and decision-making.

• Apply predictive analytics and historical audit data to enable risk-based audit planning and continuous risk monitoring.

AI & Model Governance

• Ensure all models meet enterprise standards for explainability, validation, auditability, and ongoing performance monitoring, with clear documentation of intended use and limitations.

• Lead the design and build of GenAI and LLM-based solutions, including prompt design and output evaluation, ensuring results are grounded, traceable, and subject to appropriate human review.

Data Quality, Evaluation & Monitoring

• Own feature engineering and data profiling strategies, partnering with data engineering to curate high-quality, representative datasets.

• Design and operate robust model evaluation and monitoring frameworks, including metric selection, validation, error analysis, drift detection, and ongoing performance tracking.

Stakeholder Partnership & Enablement

• Partner with Internal Audit and Technology stakeholders to align analytics with audit methodology and real-world needs.

• Translate complex analytical results into clear, actionable insights for non-technical audiences.

• Support adoption through documentation, training, and integration into audit workflows with defined review checkpoints.

What We Value

The skills that will help you succeed in this role include:

• End-to-end model delivery - ability to build, validate, deploy, and monitor models with clear explainability and auditability in a regulated environment.

• Risk-focused applied machine learning - skill in identifying patterns (trends, clusters, outliers, anomalies) and translating them into ranked, reviewable risk signals.

• Rigor in evaluation and monitoring - experience defining fit-for-purpose metrics, running thorough validations, performing error analysis, and implementing drift detection and ongoing performance tracking.

• Strong data instincts - emphasis on data profiling, feature engineering, and data quality, with close partnership with engineering to curate representative datasets.

• Responsible GenAI / LLM development - ability to iterate prompts and evaluation approaches while ensuring outputs are grounded, traceable, and subject to appropriate safeguards and human review.

• Hands-on technical excellence - expert Python skills, strong software engineering practices for reliable ML/data pipelines, solid SQL, and experience with enterprise-scale data tooling.

• Cloud-first ML execution (AWS) - experience developing and deploying machine learning solutions in AWS, particularly using Amazon SageMaker.

• Stakeholder partnership and communication - ability to translate complex analytics into clear, actionable insights aligned to audit methodology and usable by non-technical stakeholders.

Education & Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field

• 7+ years of hands-on experience in data science, machine learning, or advanced analytics, including deploying models into production

• Strong proficiency in Python and common ML/data libraries (e.g., pandas, scikit-learn, TensorFlow, PyTorch)

• Solid foundation in machine learning, statistical modeling, and software engineering best practices, including model tuning and validation

• Experience working with SQL and large-scale data platforms (e.g., Spark, Databricks)

• Hands-on experience developing and deploying models in AWS, particularly Amazon SageMaker

• Proven ability to communicate complex analytical concepts to non-technical stakeholders, including senior leaders

Nice-to-Have Qualifications

• Experience applying analytics or AI in Internal Audit, ris