Risk Modeling Services Life - AI Data Scientist

PwC•New York, NY

About The Position

At PwC, individuals in software and product innovation focus on developing cutting-edge software solutions and driving product innovation to meet evolving client needs. Those in technology delivery implement and deliver innovative technology solutions, managing the end-to-end delivery process and collaborating with cross-functional teams. This role emphasizes building meaningful client connections, managing and inspiring others, navigating complex situations, and growing personal brand and technical expertise. The individual is expected to anticipate team and client needs, deliver quality, and embrace ambiguity as opportunities for growth. Key skills include responding to diverse perspectives, using tools and methodologies for problem-solving, critical thinking, understanding project objectives, developing business context awareness, self-awareness through reflection, interpreting data for insights, and upholding professional standards. As an AI Data Scientist on the Risk Modeling Services (RMS) team, you will contribute to PwC's growing expertise in developing, validating, and testing quantitative analytics and artificial intelligence models across sectors such as insurance, financial markets, and real estate. As a Senior Associate, you will analyze complex problems, mentor junior team members, and build meaningful client relationships while navigating the intricacies of quantitative analytics and artificial intelligence. You will work with data scientists, data engineers, and computer scientists focused on AI, generative AI, and econometric/statistical modeling R&D, all working to build advanced analytical techniques that help clients optimize operations and achieve strategic goals.

Requirements

Bachelor's Degree
At least 2 years of AI, ML, econometrics, software development, or other related technical skills and professional experience

Nice To Haves

Master's degree in Statistics, Financial Mathematics, Mathematics, Electrical Engineering, Physics, Econometrics, or Computer Science
Having more than 2 years of hands-on experience with data science, ML/AI, or econometric modeling in industry or applied research settings
Experience with programming languages and environments such as Python, R, Databricks, and React
Experience with LangChain, LangGraph, LangSmith, or other agentic frameworks
Experience building and deploying machine learning models including XGBoost, random forests, and support vector machines
Hands-on experience implementing DevOps practices for data, machine learning or AI systems, including automated testing, Continuous Integration / Continuous Delivery / Deployment pipelines, and infrastructure as code
Track record of supporting business development efforts, including technical sales cycles and the client proposal process
Demonstrated experience applying regulatory and risk management standards such as SR 11-7, Colorado SB21-169, NIST, ISO 42001, and the NAIC Model Bulletin
Proven verbal and written communication skills, with the ability to translate complex technical concepts for non-technical audiences and engage with key stakeholders

Responsibilities

Contribute to the development of GenAI solutions, including prompt engineering, retrieval-augmented generation (RAG), fine-tuning, AI agents, and multi-agent systems (MAS)
Apply econometric modeling techniques such as generalized linear models (GLMs), time series analysis, and semi-parametric models (e.g., fractional response models)
During the development, validation, and monitoring of models, verify compliance with model-related regulatory requirements and standards
Enforce DevOps best practices, including version control (Git), CI/CD, test automation, infrastructure as code, and system monitoring in the development and deployment of AI solutions
Design, develop, and deploy AI models in real-world client and business environments
Monitor and research emerging AI trends, fostering an agile, forward-looking development environment
Support project delivery and work with clients to process structured and unstructured data to improve business processes, workflows, and decision-making
Support the documentation and technical analysis efforts for validators, auditors, and regulators; clearly communicate complex concepts to non-technical stakeholders
Work with cross-functional teams, data engineers, architects, and data scientists to deliver efficient, high-quality solutions aligned with client needs