Senior Data Scientist, Vice President - Corporate Functions Technology

State Street • Quincy, MA

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About The Position

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.

Requirements

• 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.
• 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 Haves

• Experience applying analytics or AI in Internal Audit, risk management, compliance, or other regulated industries
• Familiarity with model risk management, data governance, and regulatory expectations
• Exposure to MLOps practices such as CI/CD, model monitoring, and production support
• Hands‑on experience with LLMs or NLP in enterprise use cases

Responsibilities

• 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.

Benefits

• Employees are eligible to participate in State Street’s comprehensive benefits program, which includes: our retirement savings plan (401K) with company match; insurance coverage including basic life, medical, dental, vision, long-term disability, and other optional additional coverages; paid-time off including vacation, sick leave, short term disability, and family care responsibilities; access to our Employee Assistance Program; incentive compensation including eligibility for annual performance-based awards (excluding certain sales roles subject to sales incentive plans); and, eligibility for certain tax advantaged savings plans.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees