Data scientist in DevOps metrics

As a recruitment company, we are aware that every solid business is driven by people with appropriately matched skills. Our management style and partnership approach allow us to adapt flexibly to your needs and provide full support throughout our cooperation. Due to our continuous growth and the large number of recruitment projects we run for our Partners, we are looking for a person for the position of: Data Scientist / Data Engineer

• Assess currently used metrics and select new ones that are appropriate to the organization's goals
• Implement metrics end to end: definition, data source, collection automation, dashboard
• Acquire and combine data from Jira, GitLab, GitHub and Gerrit (API, SQL)
• Calculate and monitor DORA metrics, Lead Time for Changes, Cycle Time and flow metrics
• Build and develop KPI dashboards in Power BI
• Interpret results and identify bottlenecks in the process
• Translate results into business language: time-to-market, cost of delays, operational risk, customer impact
• Measure the effects of implementing GenAI tools (adoption, impact on productivity and quality, return on investment in licenses)
• Support solutions based on integration with LLMs
• Prepare recommendations for improvements and measure their effects
• Document metric definitions, methodology and reports in Confluence
• Present results to teams and management

• Experience in data analytics, Platform Engineering, DevOps or the Agile/IT processes area
• Very good knowledge of Jira (JQL, workflow, status history, dashboards) and Confluence
• Practical knowledge of GitLab (pipelines, CI/CD, deployments, Merge Requests), GitHub (repositories, Pull Requests, GitHub Actions, API) and Gerrit (code review, patchsets, review times)
• Fluent use of SQL (JOINs, aggregations, CTEs, window functions) and experience with Power BI (data modeling, Power Query, DAX, dashboards)
• Knowledge of LLM and AI technologies, understanding how language models work, their capabilities and limitations (hallucinations, data security, costs), as well as the basics of prompt engineering and RAG
• Experience integrating LLMs with applications and processes through APIs, practical knowledge of GitHub Copilot (including adoption metrics) and Microsoft Copilot
• Knowledge of solutions such as NotebookLM and Google Vertex AI
• Knowledge of DORA metrics (deployment frequency, lead time for changes, change failure rate, failed deployment recovery time, rework rate) and understanding the differences between Lead Time for Changes and Cycle Time, along with the ability to calculate them
• Ability to select and implement metrics aligned with organizational goals, analyze trends, work with medians and percentiles, and combine delivery speed indicators with stability
• Ability to translate metrics into business value (speed of delivering value, risk to customers, rework costs), formulate recommendations and KPI targets, and assess data quality before drawing conclusions

• Subsidy for a sports card
• Ongoing support from a consultant
• Employee referral program