Sales Analytics Product Manager

Your opportunity Client analytics is the engine that powers how Janus Henderson engages advisors, intermediaries, consultants, and institutional clients around the world. As Product Manager, Client Analytics, you will set the vision, strategy, and roadmap for an AI-native client intelligence portfolio that turns data into commercial outcomes — meetings booked, mandates won, clients retained, and AUM converted. A central focus of the role is delivering and scaling the firm’s next-generation sales intelligence capability, an agentic, AI-driven system that tells salespeople who to call, when, and why. It combines quantitative holdings and flow data, internal CRM intelligence, and external event signals into a ranked, actionable view of the client universe, extending proven data-driven prioritization from our most mature distribution channel to underserved channels and regions globally. Alongside this flagship capability, you will own a broader portfolio of scalable global analytics solutions used by Sales, Marketing, Distribution Strategy & Analytics, and Investment partners. You will be the trusted advisor who understands business objectives and roadmaps, and guides senior stakeholders on how best to achieve them with advanced, AI-driven analytics. Your goal is to deliver scalable global analytics solutions that meet the needs of multiple client groups. Your aim is to be become a trusted advisor who understands business objectives and roadmaps, guiding senior stakeholders on how best to achieve these objectives with advanced analytic solutions. Key responsibilities Product Vision & Strategy 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. AI-driven Sales Intelligence Delivery & Scale 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). Roadmap & Prioritization 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. Stakeholder Leadership 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. Technical & Analytical Leadership 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. Team & Talent 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. Capacity & Resource Planning 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. Product Support 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. Carry out other duties as assigned. What to expect when you join our firm Hybrid working and reasonable accommodations Generous Holiday policies Paid volunteer time to step away from your desk and into the community Support to grow through professional development courses, tuition/qualification reimbursement and more Maternal/paternal leave benefits and family services Complimentary subscription to Headspace – the mindfulness app Corporate membership to ClassPass and other health and well-being benefits Unique employee events and programs including a 14er challenge Complimentary beverages, snacks and all employee Happy Hours Must have skills Proven Product Manager experience delivering analytics, data, or AI products — ideally within financial services, asset management, or wealth management. Demonstrated success bringing AI/ML products from concept into production with measurable commercial impact. Hands‑on experience building or shipping agentic AI pipelines — including LLM‑based signal extraction from unstructured data, scoring and ranking models for prioritization, natural‑language generation for business‑facing narrative, and recommendation / next‑best‑action engines. Experience designing and operating end‑to‑end pipeline optimization, where the full system is tuned against a downstream business objective rather than each component in isolation. Strong understanding of asset management distribution dynamics across intermediary and institutional channels, and of the third‑party data sources used to drive sales prioritization (holdings, fund flows, allocation activity, mandate intelligence, market events). Experience integrating analytics products directly into the CRM workflows used by sales or relationship management teams, so insights are surfaced where work already happens. Success leveraging cloud data platforms and data analytics for decision‑making and insights. Strong understanding of data warehouse concepts and experience designing star schemas. Working knowledge of large language models, machine learning, and modern AI technologies, including practical familiarity with agent orchestration and retrieval‑augmented generation. Working knowledge of Power BI and Tableau. Experience designing products against measurable outcomes (KPIs such as meetings booked, conversion, and AUM influenced) and operating outcome‑tuned feedback loops. Proven team leadership experience working with high‑performing teams of technology professionals. Excellent executive communication and stakeholder management skills across regions and seniority levels. Certified Scrum Product Owner (CSPO) or equivalent Scrum Alliance certification. Proficiency with the Atlassian suite (Jira, Confluence, Aha!). Experience documenting processes and product artifacts, including PRDs, user stories, process flows, and business process documents. Nice to have skills Master’s degree in Engineering, Computer Science, Data Science, Finance, or a related field. Equivalent industry experience will also be considered. Experience working directly with Sales and Distribution organizations in asset or wealth management, including sales operations partnership. Familiarity with version‑controlled, modular agent pipeline frameworks that allow end-to-end optimization rather than per‑component tuning. Exposure to model governance, responsible AI, evaluation frameworks, and the regulatory landscape applicable to AI in financial services. Familiarity with public‑opportunity intelligence sources (e.g., RFP feeds, leadership‑change tracking, media monitoring) and how they can be operationalized into sales workflows. CFA, CAIA, or comparable investment industry credential. Supervisory responsibilities No Potential for growth Mentoring Leadership development programs Regular training Career development services Continuing education courses Compensation information The base salary range for this position is $145,000-$160,000. This range is estimated for this role. Actual pay may be different. This position will be open through June 30, 2026. Tagged as: Hybrid #J-18808-Ljbffr IMEA