Live opening · Posted 7 days ago

Data Product Owner

Digitain · Yerevan
Bamboohr No Full-Time
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At a glance

The key details from the original listing.

Posted 7 days ago
CompanyDigitain
LocationYerevan
Job typeFull-Time
Work modeNo
SourceBamboohr
ListedPosted 7 days ago

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About the role

Description supplied by the original job listing.

Description
We are seeking a Data Product Owner to lead the vision, strategy, and execution of our data and AI/ML products. This role bridges business stakeholders, data engineering, and data science, ensuring that our data assets, analytics platforms, and machine learning models deliver measurable value. The ideal candidate combines product management expertise with strong knowledge of data, analytics, and applied AI/ML.
Responsibilities
Define and own the roadmap for data and AI/ML products (data platforms, APIs, dashboards, predictive models, recommendation engines, NLP/vision solutions, etc.)
Translate business goals into AI/ML-enabled product strategies that drive automation, insights, and competitive advantage
Partner with business stakeholders to identify opportunities where AI/ML and advanced analytics can create value
Act as the key interface between data scientists, engineers, and business units, aligning priorities across teams
Maintain and refine product backlog; write detailed user stories with business and technical acceptance criteria
Work closely with data scientists to design, build, and deploy ML models into production
Ensure data quality, explainability, scalability, and performance are built into all products
Oversee the lifecycle of ML models (development, deployment, monitoring, retraining)
Ensure data and AI/ML products comply with internal policies and regulatory requirements (GDPR, responsible AI, gaming/financial regulations)
Have minimal knowledge in MLOps, metadata management, and data governance
Requirements
Proven experience as a Product Owner / Product Manager in data, AI/ML, or analytics-focused products
Strong understanding of data science workflows, machine learning lifecycle
Domain knowledge in iGaming, fintech, or other data-intensive industries
Understanding of responsible AI, model explainability, and bias mitigation
Ability to identify and prioritize high-value AI/ML use cases
Familiarity with data engineering and BI platforms
Hands-on exposure to AI/ML concepts (predictive modeling, NLP, computer vision, recommendation systems)
Excellent communication and stakeholder management skills, with the ability to align technical and business teams
Experience with Agile/Scrum and product backlog management

Employment type
Full-Time

Work arrangement
No

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