Back to jobsProduct Vision & Strategy
AI-driven Sales Intelligence Delivery & Scale
Roadmap & Prioritization
Stakeholder Leadership
Technical & Analytical Leadership
Team & Talent
Capacity & Resource Planning
Product Support
Carry out other duties as assigned.
Job Description
- Define and evolve the product vision for Client Analytics across the asset management distribution lifecycle — prospecting, prioritization, engagement, retention, and growth.
- Own a multi-year strategy that aligns AI-driven client intelligence with Distribution and firm-wide commercial priorities.
- Set the bar for what "great" looks like in client-facing analytics, balancing global consistency with regional and channel-specific needs across intermediary and institutional segments.
- Articulate the path from today's analytics estate to an AI-native sales operation in which client-facing prioritization, signal tracking, and outreach activity converge in a single environment.
- Lead end-to-end delivery of the firm's AI-driven sales intelligence capability, from scoping and discovery through build, internal QA, phased rollout, and ongoing optimization.
- Run embedded discovery sessions with regional sales teams to map prioritization workflows and confirm the universe of next-best-action types the system should recommend.
- Define the signal schema, the scoring objective (allocation likelihood), and persona-driven customization down to the individual client level.
- Direct the onboarding and integration of third-party data feeds--including holdings, fund flow, allocation, mandate, and market intelligence sources--into the firm's cloud data platform.
- Partner with Data Science and Engineering on the agentic pipeline that underpins the product: signal extraction agents that turn unstructured data into structured inputs; a scoring/ranking model that allocates attention across the client universe; a natural-language explanation module that generates a "why now" narrative for each contact; and a next-best-action module that recommends the appropriate engagement step.
- Surface real-time opportunity alerts (e.g., public opportunities such as RFP releases, leadership changes, fund restructuring, and M&A) into the ranked view.
- Enable user-contributed lead enrichment so sales teams can feed non-public context back into the signal pipeline.
- Embed the ranked output directly inside the CRM workflow used by sales teams, rather than alongside it, and map persona-level recommendations to the right client owner.
- Establish outcome-based feedback loops so the platform is tuned end-to-end against downstream business results--meetings booked and AUM converted--and gets sharper over time.
- Sequence rollout across regions and channels (e.g., starting with internal QA with sales ops, then deployment to international distribution teams, then expansion to institutional channels with an adapted signal stack).
- Plan, prioritize, and maintain a 12-month rolling product roadmap that balances the flagship sales intelligence build-out, BAU enhancements to the existing analytics estate, and new client analytics opportunities.
- Make data-driven trade-off decisions and develop business cases for senior stakeholders.
- Partner with the Analytics Product Owner to translate strategic priorities into deliverable epics, features, and releases.
- Ensure non-functional requirements--production readiness, observability, risk-based testing, and model governance--are scoped from the outset.
- Plan and prioritize releases, determining timing and content for each.
- Serve as a trusted advisor to Distribution leadership, Sales, Sales Operations, Marketing, Distribution Strategy & Analytics, Enterprise Data Management, and Data Science teams.
- Communicate progress, status, risks, and outcomes clearly and proactively to senior business sponsors across regions.
- Translate complex AI, agentic, and analytics concepts into commercial language and decisions.
- Act as the bridge between business stakeholders and the technical product team.
- Apply deep fluency in cloud data platforms, large language models, agentic AI pipelines, machine learning, and financial data management to shape product direction.
- Apply knowledge of data warehouse concepts and experience designing star schemas to scale solutions responsibly.
- Leverage Power BI and Tableau for high-impact data visualization where appropriate.
- Collaborate with Analytics Domain Architects, Data Architects, and the broader Architecture community to implement best practices.
- Stay abreast of emerging AI capabilities--including agent orchestration frameworks, retrieval-augmented generation, LLM evaluation, and end-to-end pipeline optimization--and translate them into product opportunities.
- Provide product leadership to a cross-functional pod of application engineers, data engineers, data scientists, data analysts, technical business analysts, and testers.
- Lead and mentor team members, with a focus on skill development and personal growth goals.
- Foster a culture of client obsession, disciplined experimentation, and measurable outcomes.
- Lead work estimation exercises and determine resourcing required to deliver planned work.
- For Enterprise projects, collaborate with Project Management colleagues to ensure resourcing supports delivery of prioritized projects and that accurate, robust delivery plans are developed.
- Serve as Level 2 or 3 support for issues that arise in your product area.
- Collaborate with stakeholders on the correction (or not) of defects, balancing impact, risk, and roadmap commitments.