Industry/Sector

Technology

Specialism

Actuarial Services

Management Level

Senior Associate

Job Description & Summary

At PwC, our people in risk and compliance focus on maintaining regulatory compliance and managing risks for clients, providing advice, and solutions. They help organisations navigate complex regulatory landscapes and enhance their internal controls to mitigate risks effectively.

In actuarial services at PwC, you will be responsible for analysing and managing financial risks for clients through statistical modelling and data analysis. Your work will generate valuable insights and recommendations to help businesses make informed decisions and mitigate potential risks.

Focused on relationships, you are building meaningful client connections, and learning how to manage and inspire others. Navigating increasingly complex situations, you are growing your personal brand, deepening technical expertise and awareness of your strengths. You are expected to anticipate the needs of your teams and clients, and to deliver quality. Embracing increased ambiguity, you are comfortable when the path forward isn’t clear, you ask questions, and you use these moments as opportunities to grow.

Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:

Respond effectively to the diverse perspectives, needs, and feelings of others.Use a broad range of tools, methodologies and techniques to generate new ideas and solve problems.Use critical thinking to break down complex concepts.Understand the broader objectives of your project or role and how your work fits into the overall strategy.Develop a deeper understanding of the business context and how it is changing.Use reflection to develop self awareness, enhance strengths and address development areas.Interpret data to inform insights and recommendations.Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements.

Key Responsibilities

Data Engineering & Modeling:

Lead the development, optimization, and maintenance of complex data pipelines to handle large-scale data integration and transformation.

Apply advanced SQL and Python to design, build, and optimize data processing systems and workflows.

Utilize strong data modeling skills to develop and implement efficient and scalable database structures.

Ensure data quality, accuracy, and security throughout all stages of data processing.

Cloud Data Solutions:

Work extensively with cloud platforms (e.g., AWS, Azure, Google Cloud) to design and deploy scalable data solutions.

Implement cloud-based data lakes, warehouses, and other data storage solutions in collaboration with data architects and cloud engineers.

Automate and optimize cloud infrastructure to enhance performance and reduce costs.

Machine Learning & AI Support:

Support data scientists and machine learning engineers by preparing data for machine learning and AI models.

Utilize basic knowledge of machine learning concepts to identify opportunities for predictive analytics and automation within data workflows.

Analytics Enablement & Financial Modeling

Prepare and transform datasets to support advanced analytics, machine learning, and actuarial modeling.

Apply knowledge of ALM, cash flow modeling, discounting, and regulatory frameworks such as IFRS 17, Solvency II, and RBC.

Support the development of financial models including stochastic modeling, scenario testing, and DCF techniques.

Collaboration & Leadership

Work closely with internal and external stakeholders to understand data requirements and deliver customized solutions.

Mentor junior data engineers and contribute to team knowledge sharing.

Participate in reviews and provide guidance on technical best practices and quality assurance.

Required Qualifications & Skills

Educational Background

Bachelor’s or Master’s degree in:

Financial Engineering, Accounting, Finance, Applied Finance, Investment Banking, Economics, Statistics, Actuarial/Financial Mathematics, Computer Science, Data Science, or other quantitative fields.

Technical Proficiency

4+ years of experience in data engineering, with strong expertise in Python and SQL.

Strong hands-on experience in:

Python (or equivalent programming languages)

SQL, Excel, and VBA

Data engineering tools (ETL, Spark, Databricks, Airflow)

Cloud platforms (AWS, Azure, Google Cloud)

Solid understanding of data foundations, including:

Data modeling, relational database design, normalization, referential integrity, and data warehousing principles

Familiarity with database design, big data processing, and data visualization tools (Power BI, Tableau, QuickSight).

Experience with developing GenAI solutions, including prompt engineering, retrieval-augmented generation (RAG), fine-tuning, AI agents, and multi-agent systems (MAS) is an added advantage.

Domain Knowledge

Solid understanding of insurance and financial services industries.

Familiarity with insurance liabilities, investment strategies, and risk management frameworks.

Experience working alongside actuarial and finance teams is a strong advantage.

Certifications

Professional certifications such as FRM or CFA, or currently pursuing these, will be an added advantage.

Soft Skills

Strong analytical and problem-solving skills with attention to detail.

Excellent communication and interpersonal skills.

Proven ability to manage time and work effectively in fast-paced, team-oriented environments.

A proactive mindset with a passion for continuous learning and professional growth.

Travel Requirements

Not Specified

Job Posting End Date