AVP, Business Solutions & Risk

About the role

The AVP of Business Solutions & Risk works for a major asset management and quantitative research firm, recruited through Gravitas Recruitment Group. The role engages senior Front Office stakeholders to define and refine business needs, translates investment questions into quantitative models, and oversees their design, implementation, backtesting and validation. It also manages demand within the assigned coverage and plans capability roadmaps for scalable research and analytics.

The AVP reports directly to senior management and works in a cross-functional team of data experts, technology partners and Front Office stakeholders. Success is measured by timely initiatives that improve business processes, advance model sophistication and optimise data use with AI and automation.

Key responsibilities

• Engage senior Front Office stakeholders to understand business needs and turn them into technical solutions.
• Sourcing and structuring specialised datasets and delivering impactful analysis and model development.
• Build and improve quantitative models supporting research, investment and trading.
• Develop and backtest models using Python and SQL, aligned with investment strategies.
• Use data visualisation tools such as Tableau and Databricks to communicate complex information.
• Support model validation and implement AI and machine learning techniques for research workflows.
• Collaborate with technology, operations and risk teams, and share insights to lift team performance.

Required profile

• Bachelor's degree in a quantitative discipline or Finance with strong quantitative content.
• CFA or FRM certification preferred.
• 6-8 years of experience in asset management, financial services or related fields.
• Proven experience developing, backtesting and validating quantitative models and datasets.
• Good understanding of asset management principles, investment strategies and market dynamics.

Required skills

• Strong Python and SQL, with knowledge of data engineering practices.
• Data visualisation tools (Tableau, Databricks).
• Fluent use of AI tools and techniques for research and modelling.
• Familiarity with quantitative research methods, including statistical inference and time-series analysis.