We are looking for a Sr. Product Manager- Tech to join us on our Finance Automation team.
As a Senior Product owner, you will have end-to-end ownership of the customer experience, from defining the product vision through building and launching the product in partnership with front-end and back-end technical teams. In this role, you will work with a team of technical and non-technical stakeholders ranging from engineers, technical program managers and designers to product managers and marketers.
Key job responsibilities Product Leadership: Listen to and advocate for the customer Define and prioritize the product roadmap (goals, customer use cases, requirements, features) Contribute to product discovery via user research, customer interviews, and data analysis Write press releases, FAQs, and business goals Ensure business alignment across all teams
Technical Engagement: Define both functional and non-functional technical requirements Use technical expertise to contribute to development discussions on product solution design and construction Engage deeply on technical decisions with engineering teams (internal and/or external)
Basic Qualifications
- 7+ years of product or program management, product marketing, business development or technology experience
- Bachelor's degree
- Experience with feature delivery and tradeoffs of a product
- Experience contributing to engineering discussions around technology decisions and strategy related to a product
- Experience managing technical products or online services
- Experience in representing and advocating for a variety of critical customers and stakeholders during executive-level prioritization and planning
Preferred Qualifications
- Experience bridging technical and business teams to collect and refine requirements, prioritize incoming work requests, and ensure all committed work is delivered on time
- Experience in building and driving adoption of new tools
- Experience performing statistical analysis of data using SQL, Excel and other tools
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
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