Technical Business Analyst

Job Title: Technical Business Analyst / Product Analyst AI & Asset / Wealth Management Domain

Location:

About the Role

Technical Business Analyst / Product Analyst

Key Responsibilities

• Collaborate with stakeholders to gather and analyze business requirements related to AI product implementation in the

  Asset and Wealth Management

  domain.
• Translate business needs into clear, actionable product and technical requirements for development teams.
• Drive AI product roadmap planning and help prioritize features with tangible business impact.
• Conduct deep-dive analyses of wealth and asset management data to identify opportunities for AI automation, personalization, and process optimization.
• Partner with data scientists, machine learning engineers, and AI architects to develop and validate AI models, especially LLM-based use cases like document summarization, intelligent chatbots, fraud detection, etc.
• Lead proof-of-concept (PoC) and pilot projects for AI/ML applications in products such as portfolio risk assessment, client service automation, KYC, compliance monitoring, etc.
• Monitor AI model performance, suggest continuous improvements, and ensure explainability and regulatory compliance.
• Stay up-to-date with the latest AI advancements (especially GPT-4/LLMs), asset and wealth management regulations, and competitive intelligence.
  

Required Qualifications

• 7+ years of experience as a Business Analyst or Product Analyst, with at least 2 years in AI/ML or Generative AI-related initiatives. 5+ Yrs experience in Data Science (Python)
• Proven experience in the

  Asset and Wealth Management

  industry (e.g., portfolio management, compliance, AML, KYC, client onboarding, investment advisory).
• Familiarity with AI tools, frameworks, and platforms (e.g., OpenAI GPT, Azure OpenAI, Hugging Face, LangChain, etc.).
• Strong understanding of AI concepts such as NLP, machine learning pipelines, LLM fine-tuning, embeddings, and vector databases.
• Ability to write detailed BRDs, PRDs, and user stories with technical depth.
• Experience working in Agile/Scrum environments.
• Proficiency in SQL, Excel, and at least one data visualization or analysis tool (e.g., Power BI, Tableau, Jupyter Notebooks).
• Excellent communication skills with both technical and non-technical stakeholders.

Preferred Qualifications

• Formal coursework or certification in AI, Machine Learning, or Data Science (e.g., Coursera, Stanford, DeepLearning.AI, etc.).
• Hands-on experimentation with GPT APIs or prompt engineering in real-world projects.
• Experience with AI use cases such as intelligent document processing, customer chatbots, RAG pipelines, or automated decision-making.

Exposure to MLOps, AI model monitoring, and explainability frameworks.