Assurance - Risk Modeling Services- AI Data Scientist- Senior Associate

A career within Actuarial Services, will provide you with the opportunity to contribute general insurance advice and consulting services to clients such as insurers, banks, regulators, and third party administrators. You’ll provide advice covering solvency, economic capital, risk management, mergers and acquisitions, company restructuring and risk control, financial modelling, underwriting, new legislation, and the efficiency of claims or reporting processes. Simply put, you might help analyse the impact that new regulation will have on an insurance company’s balance sheet or you could be revising businesses’ projections and making sure they have adequate reserves.

To really stand out and make us fit for the future in a constantly changing world, each and every one of us at PwC needs to be a purpose-led and values-driven leader at every level. To help us achieve this we have the PwC Professional; our global leadership development framework. It gives us a single set of expectations across our lines, geographies and career paths, and provides transparency on the skills we need as individuals to be successful and progress in our careers, now and in the future.

Responsibilities

As a Senior Associate, you'll work as part of a team of problem solvers, helping to solve complex business issues from strategy to execution. PwC Professional skills and responsibilities for this management level include but are not limited to:

• Use feedback and reflection to develop self awareness, personal strengths and address development areas.
• Delegate to others to provide stretch opportunities, coaching them to deliver results.
• Demonstrate critical thinking and the ability to bring order to unstructured problems.
• Use a broad range of tools and techniques to extract insights from current industry or sector trends.
• Review your work and that of others for quality, accuracy and relevance.
• Know how and when to use tools available for a given situation and can explain the reasons for this choice.
• Seek and embrace opportunities which give exposure to different situations, environments and perspectives.
• Use straightforward communication, in a structured way, when influencing and connecting with others.
• Able to read situations and modify behavior to build quality relationships.
• Uphold the firm's code of ethics and business conduct.
• Help 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).
• Utilize machine learning models including XGBoost, random forests, and support vector machines.
• During the development, validation, and monitoring of models, ensure compliance with model-related regulatory requirements and standards such as SR 11-7, Colorado SB21-169, NIST, ISO 42001, and the NAIC Model Bulletin.
• Experience with programming languages and environments such as Python, R, Databricks, and React.
• Enforce DevOps best practices, including version control (Git), CI/CD, test automation, infrastructure as code, system monitoring, and cross-functional collaboration.
• 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.
• Support business development efforts, including the technical sales cycles and the client proposal process.
• Support the development of internal GenAI assets and foundational capabilities to grow the firm’s practice.
  
  

Qualifications

Minimum Educational Qualifications:

A degree in statistics, mathematics, electrical engineering, physics, econometrics, computer science, or another related technical field.

Credentials - Good To Have

A master's degree or PhD in a related field from a premium institute is preferred but is not required.

Min years of Experience Requirement (Credential) - 3-4 years