Junior Analyst Developer - Investment Risk (Early Career)

Requirements

• Experience with implementing or exposure to any market risk analytics system (e.g., MSCI, Blackrock Aladdin, Bloomberg, etc.) is a plus
• Pursuing or completing professional certifications like CFA, FRM, PRM, or CAIA is desirable.
• Bilingualism (English and French) is a strong asset. If the successful candidate is in Québec, proficiency in both languages will be required to support clients from various provinces outside of Quebec.
• When you join our team:
• We'll empower you to learn and grow the career you want.
• We'll recognize and support you in a flexible environment where well-being and inclusion are more than just words.
• As part of our global team, we'll support you in shaping the future you want to

Benefits

Additional Information

We are seeking skilled professionals to join the Investment Risk Oversight team within Manulife's Global Wealth & Asset Management (GWAM) segment. In this role, you will collaborate with team members and internal stakeholders to enable independent oversight of investment risks managed or manufactured within Manulife's GWAM segment. You will design, build, and maintain Python applications, data pipelines, and AI-enabled tools that support investment risk reporting, analytics, and governance. Working closely with specialists across Portfolio, Market, Liquidity, and Counterparty Risk, you will help strengthen the underlying risk data infrastructure, ensuring data quality, scalability, and sustainable operation of applications and data assets. You may apply Large Language Models (LLMs) to transform structured and unstructured financial data into actionable insights and clear risk narratives aligned with global and regional reporting needs. Position Responsibilities: Risk Infrastructure, Data Management & Automation (30%): Maintain and enhance risk infrastructure for collecting, storing, transforming, and managing risk data, including data lakes, data warehouses, and data pipelines, ensuring data integrity and accessibility, primarily using Python, SQL, and related tools. Design and implement workflows to automate data transformation, extract structured information from unstructured sources (e.g., PDFs, Excel, SharePoint), and generate insights that improve the efficiency of risk reporting and decision-making. Work closely with technology, data, and other relevant teams to ensure smooth investment data flow and integration, and to support the integrity of risk analytics produced from the risk systems. Risk Reporting (30%): Contribute to investment risk reporting and analytics for GWAM and its underlying business units / regions, ensuring accurate and timely reporting coupled with concise narratives of key observations and areas of concern. Develop dashboards and analytical tools using data visualization platforms (e.g., Power BI, Tableau) and Python-based libraries. Process Optimization (10%): Manage and prioritize a pipeline of development and enhancement requests. Enhance data quality and improve the efficiency of risk processes to ensure accurate and reliable risk assessments. Identify, evaluate, and implement tools and platforms to streamline risk management processes such as automating risk reporting capabilities. Stakeholder Engagement (20%): Collaborate with investment risk managers and stakeholders to understand requirements and translate them into technical specifications. Facilitate the effective operation of regional investment risk forums by preparing discussion materials and engaging in discussions with forum members to address key observations and concerns. Team Management and Development (5%): Provide guidance, coaching, and informal mentorship to support team development and knowledge sharing. Regulatory Compliance and Industry Standards (5%): Stay abreast of regulatory requirements and industry standards related to investment risk oversight, ensuring compliance and adherence to best practices. Assess market developments to evaluate their impact on risk exposures and ensure timely, accurate reporting to stakeholders. Required Qualifications: A bachelor's or master's degree in finance, economics, mathematics, statistics, engineering or a related field. Recent graduates or early-career professionals (0-3 years), preferably with experience in finance or financial markets (e.g., asset managers, financial institutions, or fintech firms). Demonstrated proficiency in Python (including relevant libraries) and SQL. Demonstrated experience building AI-enabled solutions, including practical experience integrating LLM APIs (e.g., Azure OpenAI, OpenAI) for use cases beyond basic chat, such as advanced prompt engineering, retrieval-augmented generation (RAG), structured data extraction, or document processing. Foundational understanding and application of ML and LLMs. Working understanding of core investment concepts, including market risk, liquidity risk and exposure analysis. Experience with data visualization tools such as Power BI, Tableau, or similar data visualization tools.

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