About this role
Role Overview:
We are seeking a visionary, strategic, and execution-oriented leader to serve as the Head of AlphaGen Investor Research Product Engineering. This role centralizes end-to-end ownership of the AlphaGenâs research product estate spanning research lifecycle with an explicit focus on eliminating fragmented operational burden and establishing a single accountable organization for AlphaGenâs modernization and future growth.
You will drive a unified product and transformation strategy that accelerates evolution toward a cloud-native, AI-first research ecosystem. Key initiatives include retiring legacy tooling, modernizing core runtimes, and creating scalable self-service workflows that shorten research-to-production from months to hours. This transformation enables AlphaGenâs Investor Research Solutions & Services to focus on high-value quantitative partnership with the Portfolio Management Group (PMG), while Investor Research product (under your leadership) provides a robust foundation, reusability, and productized research capabilities to support and scale alpha generation efforts.
AlphaGen Investor Research Product is PMGâs strategic research-technology engine. In this role, you will lead the organization responsible for delivering a cloud-native, AI-powered, self-service suite of products that accelerates the end-to-end lifecycle of data, signals, and models all while working closely with the Alphagen platform team in providing the operational backbone required for reliability, resiliency, security, and scale.
Key Responsibilities:
Strategic Leadership & Transformation Execution
Strategy Development: Develop and execute a cohesive engineering strategy that aligns AlphaGenâs product direction with major transformation initiatives (e.g., cloud migration, AI-first workflows, standardization, legacy system retirement).
Clear Ownership: Establish clear product ownership, accountability, and governance across the entire AlphaGen investor research product estate
Partnership Building & Cross-Functional Collaboration
Cross-Organization Partnerships: Forge and nurture strong partnerships across stakeholders, broader Aladdin product and platform engineering organizations to drive shared roadmaps, shared outcomes, and frictionless execution of initiatives.
Governance & Standards: Strengthen collaboration with internal governing bodies and central teams to ensure consistent standards, regulatory compliance, and seamless integration with broader enterprise systems.
User Engagement & Adoption (Self-Service by Default)
User-Centric Design: Drive user adoption strategies in partnership with product management and key stakeholders, creating a âsingle front doorâ experience for researchers to access data, tools, documentation, and support covering spectrum of research personas.
Frictionless Pipeline: Deliver a frictionless research-to-production pipeline by productizing building blocks to enable data onboarding, signal development, model validation, production release, thereby enabling researchers to go from idea to production with minimal hand-offs.
Product Engineering Leadership (What Researchers Use)
Core Frameworks & SDKs: Own and evolve the core libraries, frameworks, and SDKs that enable consistent signal and model development (e.g., standardized APIs, templates, archetypes), ensuring researchers have a cohesive development experience.
AI-Enabled Tooling: Lead development of AI-powered research tools and automation (spec-to-signal workflows, validation agents, automated quality checks) to shift scaling from people-driven processes to platform-driven capabilities.
Model Lifecycle Governance: Own end-to-end model lifecycle governance tooling, from experiment to validation to production, including configuration management, metadata tracking, reproducibility, and control frameworks.
Product Infrastructure Leadership (Systems the Platform Runs On)
Foundational Systems Collaboration: collaborate with the Alphagen platform team to help optimize the foundational systems that power AlphaGenâs products, including:
Compute & Runtime: scalable GPU/CPU execution environments, define efficient scaling policies, drive performance optimizations, and manage resource allocation models for diverse research workloads.
Observability & Operations: end-to-end telemetry and monitoring, enable proactive incident detection, and automate recovery mechanisms to ensure high platform uptime and resiliency.
DevOps & CI/CD: Champion infrastructure-as-code, robust deployment pipelines, and standardized build/test/release workflows to accelerate delivery and improve reliability of platform updates.
Reliability & Production Standards: apply resiliency patterns, and harden systems to enterprise production standards for security and stability.
Operational Automation: Expand automation for operational tasks (data quality checks, pipeline health monitoring, backfills, change management) to reduce manual intervention and error risk.
Legacy Modernization & Tech Stack Evolution
Cloud Migration: key player in the end-to-end product modernization journey, including the migration from legacy systems to cloud-native architectures, and the timely retirement and consolidation of legacy tools.
Next-Gen Platform Architecture: Drive an API-first, cloud-native platform design to improve scalability, performance, and interoperability across the ecosystem. Leverage AI/ML where it materially improves speed, quality, and control (e.g., intelligent automation, anomaly detection).
Market and Business Insight / âNorth Starâ Alignment
Industry & Internal Insight: Stay attuned to market trends and internal demand signals, such as the rising need for adaptable alpha-generation platforms and AI-driven research capabilities.
Define the North Star: Partner with senior leadership to define and communicate a clear business âNorth Starâ for AlphaGen Product Engineering â reduce operational barriers, enable rapid iteration, scale platform services, and ultimately capture more alpha opportunities for the firm.
Qualifications:
Education: Bachelorâs degree in Computer Science, Engineering, or a related field; or equivalent practical experience. An advanced degree is a plus.
Strategic Engineering Leadership: Proven experience leading large-scale engineering organizations through product transformation initiatives (e.g., platform modernization, cloud migration, operating model changes). Demonstrated success in setting vision and executing across complex, multi-year technology programs.
Technical Depth (Strong understanding of):
Collaboration & Influence: Demonstrated strength in partnership-building and cross-functional collaboration. Able to influence and align product teams, platform/infrastructure teams, and governance bodies around a common vision and roadmap.
Execution in Complexity: Ability to operate in complex market and organizational contexts, translating strategic objectives into actionable plans. Proven track record of driving innovation, navigating ambiguity, and delivering measurable outcomes at scale.
Leadership Attributes:
This leader exemplifies operating principles that set the tone for how the team works and evolves:
AI-First, Human-in-the-Loop: Embraces AI-driven solutions as a default for efficiency and scale, while keeping expert human judgment in critical loops to ensure quality and trust.
Platform Over People: Prioritizes building robust platforms and automation over relying on individual heroic efforts, enabling sustainable and scalable growth.
Single Front Door: Advocates for a unified user experience where researchers and stakeholders have one intuitive portal for all their needs, simplifying access and support.
Standardization Everywhere: Drives consistency in tools, processes, and data across the board, reducing complexity and improving interoperability and maintainability.
Self-Service as the Default: Champions self-service capabilities, empowering users to accomplish tasks independently (from data ingestion to model deployment) without bespoke engineering work.
Deep Co-Ownership with PMG: Fosters a culture of partnership with the Portfolio Management Group, aligning closely on priorities, sharing roadmaps and KPIs, and co-owning outcomes to ensure the platform truly meets investment teamsâ needs.
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