Be a leader committed to understanding customer needs with your advanced knowledge of product development, design, data analytics, and responsible AI delivering impactful, compliant, and scalable solutions.
You enjoy shaping the future of product innovation as a core leader, driving value for customers, guiding successful launches, and exceeding expectations. Join our dynamic team and make a meaningful impact by delivering high quality products that resonate with clients leveraging AI across machine learning, NLP, retrieval, and generative techniques (with agentic orchestration as one option among many).
Job summary
As a Product Manager in Legal & Regulatory Control Operations (LRCO) Product
, you are an integral part of the team that innovates new product offerings and leads the end-to-end product life cycle. As a core leader, you act as the voice of the customer and develop profitable products that provide measurable value. You guide successful launches, collect feedback, and ensure top tier client experiences delivering a broad AI toolkit including predictive analytics, machine learning, natural language processing, retrieval augmented generation, workflow automation, and agentic approaches as appropriate. With a strong commitment to scalability, resiliency, stability, and responsible AI, you collaborate closely with cross functional teams to deliver compliant, high-quality products that exceed customer expectations.
Job responsibilities
- Own and prioritize the product backlog and roadmap, balancing AI capabilities with core features to meet strategic goals and regulatory constraints.
- Assist discovery and research to identify AI use cases across ML, NLP, information retrieval (e.g., RAG), optimization, predictive analytics, and workflow automation evaluating build/buy/partner options (agentic methods included when they add value).
- Establish success metrics and instrumentation, including adoption, accuracy/precision/recall, time to resolution, cost to serve, reliability/latency, and risk posture.
- Drive cross functional delivery with Engineering, Data Science/ML, UX, Operations, Compliance, Legal, and Risk to ship AI capabilities, classification, anomaly detection, recommendations, and decision support with human in the loop where appropriate.
- Champion Responsible AI, ensuring data governance, privacy, security, explain ability, fairness, evaluation rigor, monitoring, and model risk management across the model lifecycle.
- Translate customer pain points into AI powered solutions for example regulatory change intelligence, control monitoring, case triage/routing, knowledge retrieval, evidence synthesis, redaction/PII detection and validate via pilots and A/B tests. Support and troubleshooting partner with teams to resolve issues, prioritize fixes, and drive continuous improvement based on telemetry and customer feedback.
- Author epics, user stories, and acceptance criteria (including offline/online evaluation plans) and manage delivery using Agile practices and tools (Jira, Confluence).
- Plan change management for AI features (enablement, documentation, training, support readiness) and track business impact post launch.
- Manage platform and vendor ecosystem (LLM platforms, vector stores, orchestration frameworks, MLOps/monitoring, analytics) to optimize cost, performance, and SLAs.
- Coach, mentor and motivate project team members, influencing them to act and take accountability for their assigned work
Required qualifications, capabilities, and skills
- Leverage 8+ years of experience or equivalent expertise in product management or a relevant domain.
- Apply advanced knowledge of the product development lifecycle, design, and data analytics.
- Lead discovery, ideation, strategy, requirements, and value management in complex environments.
- Operate effectively in a highly matrixed, complex organization.
- Translate AI technologies, including machine learning, NLP, and generative AI, into business value.
- Utilize Agile processes and tools like Confluence, JIRA, and Git.
- Collaborate with Data Science/ML and Engineering teams; be familiar with AI platforms and tooling.
- Make data-driven decisions using experimentation, statistical thinking, and telemetry.
- Apply knowledge of Responsible AI, privacy, security, compliance, and model risk management.
- Communicate complex AI concepts to non-technical stakeholders and senior leaders effectively.
- Adapt and solve problems with strong organization and execution skills in dynamic environments.
