Technical practice
Applying LLMs to Investment Research
What to coverExplain how you would use an LLM to extract key insights from unstructured financial reports or market news. Discuss the importance of data accuracy, handling hallucinations, and the trade-offs between using pre-trained models versus fine-tuning for specific financial domains.
Prompt Engineering Strategies
What to coverDescribe your approach to designing prompts for complex tasks like summarizing market data or generating investment reports. Mention techniques such as few-shot prompting, chain-of-thought reasoning, and how you validate the consistency of the model's output.
AI Agent Frameworks
What to coverDiscuss your experience or understanding of AI agent frameworks and how they can be used to automate multi-step workflows. Explain how you would design an agent to monitor market data and trigger an automated alert or report generation process.
Automation and Scripting for Data Aggregation
What to coverDescribe a scenario where you would use Python or other scripting languages to aggregate data from multiple sources. Focus on how you handle data cleaning, error handling, and ensuring the reliability of the automated pipeline.
Evaluating AI Tool Performance
What to coverHow do you measure the success of an AI-driven tool built for a portfolio manager? Suggest metrics beyond simple accuracy, such as time saved, reduction in manual errors, or the quality of the generated insights.
Managing Data Privacy and Security
What to coverWhen building tools for an asset management firm, what considerations must be made regarding data privacy and security? Discuss the risks of feeding sensitive financial data into public LLMs and how you would mitigate these risks.
Workflow Documentation and Knowledge Transfer
What to coverWhy is it critical to document prompts and workflows in a team environment? Explain your process for creating clear, maintainable documentation that allows other team members to understand and iterate on your AI solutions.
Understanding Capital Markets
What to coverBriefly explain how your technical skills can be applied to solve a specific problem in project finance or capital markets. Suggestion: Research common pain points in investment management, such as the time spent on manual data entry or monitoring market volatility.
Behavioural practice
Translating User Pain Points into Technical Solutions
What to coverDescribe a time when you had to interview a non-technical stakeholder to understand their workflow challenges. How did you translate their requirements into a functional technical solution, and how did you ensure it met their needs?
Self-Learning and Adapting to New AI Tools
What to coverThe AI field evolves rapidly; provide an example of how you have independently learned a new framework or tool. Explain your process for staying updated and how you apply new knowledge to practical projects.
Collaborating with Diverse Teams
What to coverShare an experience working in a collaborative environment where you had to communicate technical concepts to team members with different backgrounds. How did you ensure alignment and successful project delivery?
Handling Ambiguity in Projects
What to coverDescribe a situation where you were given a project with limited guidance or ambiguous requirements. How did you define the scope, prioritize tasks, and move forward to deliver a working prototype?