IT company Krok is looking for a Product Manager (GenAI & Data) with experience in large systems (integrator, vendor, enterprise), who knows how to launch new directions and think in terms of product.
Tasks:
- Formation and validation of product hypotheses in the area of data and GenAI solutions;
- Transformation of project cases into repeatable product models;
- Formalization of functional and non-functional requirements;
- Linking business goals, architectural solutions, and data into a unified product logic;
- Participation in the formation of MVP, roadmap, and scaling models;
- Work with internal teams: architects, development, presale, sales;
- Participation in launching new product directions and bringing them to the B2B segment.
Experience Requirements:
- 3–6 years of experience as a product analyst, product manager, system analyst, or similar position;
- Mandatory experience in a large corporate environment (integrator, vendor, enterprise);
- Participation in the launch of a new product or product line (from idea to implementation/replication);
- Experience with complex IT systems and cross-functional teams;
- Understanding of MVP principles, hypothesis validation, and bringing solutions to the B2B market;
- Knowledge of corporate data management processes, experience with DG, DWH, BI, Data Lakehouse, Data Lake solutions.
Approach and Mindset Expectations:
- Systems thinking and the ability to see the solution as a whole;
- Ability to structure uncertainty and turn it into a working plan;
- Ability to formulate measurable business value;
- Willingness to take responsibility for the direction, not just the document;
- Engagement with modern technologies;
- It is fundamentally important for us that the candidate is personally interested in the development of modern technological approaches.
Candidate Expectations:
- Actively use modern GenAI tools in daily work and personal efficiency (not episodically, but regularly over the last 6–12 months);
- Follow the development of the technological market and try new tools in practice;
- Consider technologies as a working tool, not as a theoretical subject;
- Deep expertise in specific GenAI stacks is not mandatory — we will learn and develop them together. More important is internal interest, speed of learning, and the ability to apply technologies to real tasks.
Work Results:
- Launched and validated product initiatives;
- Repeatable solutions for the B2B segment;
- Increased commercial efficiency of directions related to data and GenAI;
- Formed product logic around complex integration cases.
