About Starya
Starya develops AI for complex operations. We connect artificial intelligence, software, and data to transform real business processes with security, reliability, and control.
Our platform, NebulaOS, is the foundation for building, integrating, and operating these solutions. Our work combines engineering close to customers, product development, and a foundation of data, infrastructure, and security.
The challenge
Build an understanding of which problems the platform should solve, for whom, and in what order. In enterprise AI, value depends on the process, integrations, and the ability to use the product. You turn these dependencies into scope decisions, outcome hypotheses, and learning after each delivery.
What you will do
- Investigate work with users, operational owners, and FDEs. Produce a problem map covering tasks, exceptions, participants, and the costs of current difficulties, distinguishing who buys, configures, and uses the solution and the need behind each request.
- Prioritize problems by recurrence across customers, impact on the process, business direction, and feasibility. Compare alternatives with engineering, including configuration, integration, or product changes, and make the reasons for proceeding or deferring visible.
- Write a specification for the delivery scope, with expected outcomes and acceptance scenarios. For example, enable a person to identify an interrupted execution and choose an authorized recovery action, with confirmation of its effect and handling of relevant exceptions.
- Define metrics and an instrumentation plan with data and specialists, recording the event, population, and period. Track task completion, intervention needs, and time to first useful use, investigating whether using the platform improved the process.
- Prepare adoption with Design, Client Engineering, and users: identify dependencies on data, access, configuration, and changes in routine. Follow where usage stalls and decide whether the response requires product improvement, guidance, or an integration adjustment.
- Revisit hypotheses after delivery using observed behavior, feedback, and quality evidence. Communicate what was learned, update priorities, and decide when to expand, correct, or end an approach, with clear criteria for the next decision.
What we look for
- Experience leading discovery and product decisions, with examples where observing users or usage outcomes changed the selected problem, proposed solution, or priority.
- Ability to structure enterprise problems, identify different participants, and understand process dependencies. Clear writing about context, hypotheses, alternatives, and criteria that help the team make decisions.
- Foundations in metrics and outcome analysis, including population definitions, comparisons, and data limitations. Ability to investigate alternative explanations before attributing a change to the product.
- Practice partnering with engineering and design to negotiate scope, capacity, risk, and sequencing. Ability to explain commitments and incorporate technical evidence into prioritization.
- Experience following adoption and speaking with users after delivery. Ability to connect configuration, comprehension, or integration difficulties to concrete decisions about the evolution of the experience.
Additional experience
- Experience with B2B products, platforms, or operations where different teams participate in buying, configuring, and using the solution, and value depends on integration and changes in routine.
- Experience with AI products, considering variable quality, human intervention, execution costs, and confirmation of actions when defining acceptance criteria and tracking outcomes.
- Experience turning deployment lessons into reusable capabilities, negotiating specific requests, and documenting decisions that help customers and teams understand product direction.
Your impact
An investigation may lead to simplifying integration setup before expanding automation. Your work makes that choice understandable and makes it possible to check whether more teams can complete the task after the change. Adoption, remaining difficulties, and effects on the process guide where to invest next.
Who you build with
You partner with the Engineering Lead, who leads people, capacity, and technical engineering decisions. You work with Design, QA, AI Research, data, and Client Engineering to build evidence and follow adoption. Align direction and priority conflicts with the CTPO and business owners.