Business Analyst (Cybersecurity) @ Mindbox S.A.

One of the most sought-after challenges in the tech industry today is to scientifically and reliably demonstrate a link between the use of GenAI-based coding assistants (e.g., GitHub Copilot) and actual developer productivity. Despite millions of users globally, no organization has yet published statistically robust evidence to confirm this relationship.

This role builds on the foundation of a successful initial proof of concept, where – thanks to mature tracking of SDLC, deployment, and DORA metrics, combined with the work of data scientists – such a correlation has been established with statistical confidence.

Your mission will be to take this early research forward : mature it, operationalize it, and transform insights into actionable outcomes. The goal is to identify which use cases, engineering patterns, or team configurations benefit most or least from GenAI coding assistants — both at the individual developer and team (POD) level.

In parallel, you will explore additional opportunities to optimize performance across engineering teams. Your work will directly feed into broader adoption efforts, including training, education, coaching, mentoring, and community events, as well as integration with an internal expert hub that curates knowledge and connects subject-matter experts in the GenAI space.

Why this role matters :

Success here will lead to industry-defining insights into how GenAI coding assistants impact developer effectiveness. The outcomes will play a critical role in improving the productivity of over 20,000 engineers and help set a new benchmark for how software teams adopt and measure the value of GenAI in real-world development environments.

• Self-starter.
• Track-record of developing knowledge acquisition, knowledge transfer, communities of interest, processes and content.
• Proven ability to deliver globally across matrix-based organisations
• Strong Influential communication and interpersonal skills, able to operate well across cultures and in multicultural, diverse work environment
• Proficiency in verbal and written English, especially proficient in email communication and in occasionally rewriting English content when written by a non-native speaker
• Willing to learn, explore and implement new and emerging technologies
• Ability to work in a fast-paced dynamic environment to deliver high quality results under tight schedule and pressure
• Proven experience in data analysis, particular in software development context
• Strong understanding of SDLC, Deployment and DORA metrics and their relevance to developer productivity
• Experience in translating data analysis into actionable business or operational insights
• Familiarity with GenAI technologies, specifically coding assistant like GitHub Copilot
• Exposure to data Analytics and processing using Python / R / SQL / Jupyter notebook or similar toolkits
• Deep understanding of data modelling
• Exposure to Machine Learning Libraries e.g. Scikit-learn / XGboost / Keras / Pytorch or similar

One of the most sought-after challenges in the tech industry today is to scientifically and reliably demonstrate a link between the use of GenAI-based coding assistants (e.g., GitHub Copilot) and actual developer productivity. Despite millions of users globally, no organization has yet published statistically robust evidence to confirm this relationship.

This role builds on the foundation of a successful initial proof of concept, where – thanks to mature tracking of SDLC, deployment, and DORA metrics, combined with the work of data scientists – such a correlation has been established with statistical confidence.

Your mission will be to take this early research forward : mature it, operationalize it, and transform insights into actionable outcomes. The goal is to identify which use cases, engineering patterns, or team configurations benefit most or least from GenAI coding assistants — both at the individual developer and team (POD) level.

In parallel, you will explore additional opportunities to optimize performance across engineering teams. Your work will directly feed into broader adoption efforts, including training, education, coaching, mentoring, and community events, as well as integration with an internal expert hub that curates knowledge and connects subject-matter experts in the GenAI space.

Why this role matters :

Success here will lead to industry-defining insights into how GenAI coding assistants impact developer effectiveness. The outcomes will play a critical role in improving the productivity of over 20,000 engineers and help set a new benchmark for how software teams adopt and measure the value of GenAI in real-world development environments.

Review the current GenAI Coding Assistant (CA) Productivity Analysis proof of concept and contribute to shaping its future roadmap and delivery strategy., Identify high-impact use cases and technical patterns where GenAI CAs provide the greatest benefit to developers and teams., Analyse and document key challenges, barriers, enablers, and best practices for the effective adoption of GenAI CAs., Collaborate with the GenAI CAs programme manager to help shape a data science strategy for measuring GenAI CA effectiveness and ROI., Leverage internal software development lifecycle (SDLC), deployment, and engineering performance metrics (e.g., DORA) to gather, analyse, and interpret productivity-related data., Work closely with data analysts and data scientists to refine analytical models and methodologies for assessing GenAI CA impact., Define a delivery approach, including a phased implementation plan, clear objectives, and operational rollout of minimum viable products (MVPs)., Partner with the programme manager to : , Communicate actionable insights and findings to senior stakeholders in a clear and compelling way., Translate insights into tangible actions by supporting delivery teams in designing and executing sub-projects aimed at improving developer productivity and maximizing GenAI CA value.] Requirements : Data analysis, SDLC, GitHub, Data analytics, R, Jupyter Notebook, Machine learning, scikit-learn, Keras, PyTorch, Agile Tools : Agile. Additionally : Sport Subscription, Private healthcare, Life insurance, Training budget.