Študentsko delo v NLB – Podatkovna analitika in UI

📍 Job Overview

Job Title: Študentsko delo v NLB – Podatkovna analitika in UI

Company: NLB Portal

Location: Ljubljana, Slovenia

Job Type: Internship

Category: Data Analytics & Artificial Intelligence Operations

Date Posted: 2026-09-20

Experience Level: Entry-Level (0-2 years)

Remote Status: On-site

🚀 Role Summary

• This is an entry-level student role focused on data analytics and artificial intelligence (AI) within a leading banking group.

• The position offers hands-on experience in developing predictive models, AI agents, and data solutions to address real-world business challenges.

• The role involves preparing business analyses, creating data visualizations, and contributing to the development and maintenance of AI-driven systems.

• It's an opportunity to gain practical experience with cutting-edge technologies and tools used by top development teams in a "Top Employer" certified environment.

📝 Enhancement Note: This role is specifically tailored for students, emphasizing practical application of academic knowledge in a professional setting. The "UI" in the title likely refers to User Interface or User Interaction in the context of presenting data and AI solutions, rather than a specific technical UI framework. The operations focus is on the application of data analytics and AI to business problems, rather than the core IT infrastructure management.

📈 Primary Responsibilities

• Prepare and conduct business analyses for ongoing development projects, ensuring data-driven insights inform strategic decisions.

• Present complex information clearly and concisely, utilizing effective data visualizations to create impactful reports for stakeholders.

• Collaborate with development teams on the design, testing, and maintenance of predictive models to forecast business trends and outcomes.

• Participate in the development, testing, and ongoing maintenance of AI agents and advanced AI solutions, contributing to innovation within the bank.

• Support the integration of new technologies and data solutions into existing banking operations.

• Assist in data cleansing, preparation, and transformation processes to ensure the accuracy and reliability of datasets used for analysis and model development.

📝 Enhancement Note: The responsibilities highlight a blend of analytical tasks, reporting, and direct involvement in AI/ML development lifecycle stages, all within a student capacity. The emphasis on "business analyses" and "data visualizations" points towards a role that bridges technical skills with business communication.

🎓 Skills & Qualifications

Education: Currently pursuing a degree in a relevant field such as Computer Science, Data Science, Mathematics, Statistics, Economics, or a related quantitative discipline.

Experience: While specific professional experience is not required, demonstrable academic projects or personal initiatives involving data analysis, programming, or AI are highly valued.

Required Skills:

• Proficiency in Python programming for data analysis and scripting.

• Familiarity with SQL for database querying and data manipulation.

• Understanding of Large Language Models (LLMs) and general concepts of Artificial Intelligence.

• Basic understanding of data analysis principles and business intelligence concepts. Preferred Skills:

• Experience working with Azure Databricks for data engineering and machine learning workflows.

• Knowledge of Azure cloud services and their applications.

• Familiarity with data visualization tools and techniques.

• Experience with developing or testing predictive models.

• Exposure to AI agent development or related frameworks.

📝 Enhancement Note: The qualifications are geared towards students with a strong foundational understanding of programming and AI concepts, with a preference for those who have already engaged with cloud platforms like Azure. The "0-2 years" experience level is appropriate for student roles, focusing on potential and learning ability.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase academic or personal projects demonstrating proficiency in Python for data analysis and manipulation.

• Include examples of SQL queries used for data extraction, transformation, or analysis.

• Present projects involving the application of AI or machine learning concepts, ideally with explanations of the problem, methodology, and outcomes.

• If available, include examples of work done on Azure Databricks or other cloud-based data platforms.

• Provide clear documentation of project scope, tools used, and any quantifiable results or insights gained. Process Documentation:

• For any project presented, clearly outline the steps taken from data acquisition to insight generation or model deployment.

• Detail any data cleaning, feature engineering, or model validation processes employed.

• Explain how insights were communicated or how the developed models were intended to solve a specific business problem.

📝 Enhancement Note: For a student role, the "portfolio" is expected to be primarily academic or project-based. The focus will be on demonstrating foundational skills and a proactive approach to learning and applying new technologies, rather than extensive professional project experience.

💵 Compensation & Benefits

Salary Range: 10 EUR gross per hour. This is a standard rate for student work in Slovenia, reflecting the entry-level nature of the role and the part-time commitment typical of student employment.

Benefits:

• Mentorship: Benefit from guidance by experienced professionals in data analytics and AI.

• Professional Development: Opportunities to learn and grow within a leading financial institution.

• Continuous Education: Access to ongoing training and development resources to enhance skills.

• Top Employer Certification: Work in an environment recognized for its excellent HR practices and employee development.

• Family-Friendly Company Certification: Experience a workplace that values work-life balance and employee well-being.

• Sustainable Banking Practices: Contribute to an organization committed to responsible banking principles and a sustainable future.

Working Hours: Standard full-time equivalent (40 hours/week) is implied for the student role, though actual hours will be part-time, aligned with student availability and academic commitments. Flexibility will be provided to accommodate study schedules.

📝 Enhancement Note: The salary is explicitly stated and is competitive for student work in Ljubljana. The benefits are highly attractive, emphasizing the company's commitment to employee growth, well-being, and ethical practices, which are significant draws for early-career professionals.

🎯 Team & Company Context

🏢 Company Culture

Industry: Banking and Financial Services. NLB is a leading universal banking group with a strategic focus on Southeast Europe (SEE). The company is committed to economic development, innovation, and sustainability.

Company Size: NLB d.d. is part of a larger international banking group, indicating a significant organizational scale with established processes and resources, yet maintaining a connection to local markets.

Founded: NLB d.d. is the parent bank of the NLB Group, which operates across SEE. The group's history and evolution reflect a long-standing presence and expertise in the region's financial sector.

Team Structure:

• The role is within a department focused on Data Analytics and Artificial Intelligence, likely part of a larger IT or digital transformation division.

• The student will work alongside experienced data scientists, AI engineers, and business analysts, reporting to a team lead or manager.

• Collaboration will be extensive, involving cross-functional teams within the bank to understand business needs and implement data-driven solutions. Methodology:

• Data analysis will be central, using quantitative methods to extract insights and support decision-making.

• Workflow planning will involve contributing to project timelines and task execution within development cycles.

• Automation and efficiency practices will be observed and potentially implemented, especially concerning AI agents and predictive models.

Company Website: https://www.nlb.si/en

📝 Enhancement Note: NLB positions itself as more than just a financial institution; it emphasizes its role in regional development, sustainability, and employee well-being. This context is crucial for candidates to understand the company's values and operational ethos. The "Top Employer" and "Family-Friendly" certifications highlight a strong focus on employee experience.

📈 Career & Growth Analysis

Operations Career Level: This is an "Intern" or "Student Worker" level role, designed as an entry point into the field of data analytics and AI within a structured corporate environment. It's an opportunity to gain foundational experience and explore career interests.

Reporting Structure: The student will likely report to a senior member of the Data Analytics or AI team who will act as a mentor, providing guidance and oversight for assigned tasks.

Operations Impact: While direct impact will be on specific projects, the work contributes to the bank's broader goals of innovation, efficiency, and data-informed decision-making. Students can see how their contributions to developing models and analyzing data help solve real business problems and improve customer experiences.

Growth Opportunities:

• Skill Specialization: Deepen expertise in Python, SQL, Azure Databricks, and LLMs through practical application and mentorship.

• Industry Exposure: Gain invaluable experience in the banking sector, understanding its unique data challenges and opportunities.

• Networking: Build professional relationships with experienced data professionals and potential future employers within NLB or the broader NLB Group.

• Potential for Future Roles: Successful interns may be considered for future graduate roles or further internships within NLB, based on performance and business needs.

📝 Enhancement Note: The growth analysis focuses on the learning potential and foundational career development this student role offers, rather than immediate advancement. The emphasis is on skill acquisition and industry exposure.

🌐 Work Environment

Office Type: On-site work in Ljubljana. NLB is a large, established financial institution, suggesting a professional office environment.

Office Location(s): Ljubljana, Slovenia. Specific office details would be provided upon engagement.

Workspace Context:

• The workspace will be collaborative, involving interaction with team members and potentially other departments.

• Access to necessary tools and technology, including development environments like Azure Databricks, will be provided.

• Opportunities for informal learning and knowledge sharing with experienced colleagues will be abundant.

Work Schedule: While the role is advertised with an implied full-time equivalent, student positions typically involve part-time hours (e.g., 20 hours/week) that can be flexibly arranged to accommodate academic schedules. This flexibility is a key aspect for student roles.

📝 Enhancement Note: The on-site requirement suggests a preference for direct collaboration and immersion in the company's operational environment. The "Top Employer" and "Family-Friendly" certifications imply a supportive and well-equipped workspace.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Review of CV and motivational letter to assess qualifications and interest.

• Technical Interview: Discussion of Python, SQL, AI/LLM knowledge, and potentially a small coding exercise or scenario-based problem.

• Behavioral/Situational Interview: Assessment of key competencies like reliability, responsibility, initiative, collaboration, and customer orientation. This may involve discussing academic projects or hypothetical scenarios.

• Team/Manager Interview: To assess cultural fit, understand motivations, and discuss project alignment.

Portfolio Review Tips:

• Highlight Relevant Projects: Select academic or personal projects that best showcase your Python, SQL, and AI/LLM skills.

• Structure Your Presentation: For each project, clearly articulate the problem statement, your approach, the tools used, the results, and any lessons learned.

• Quantify Impact: If possible, demonstrate the outcome or impact of your project, even if it's academic (e.g., improved accuracy, efficiency gain, insightful analysis).

• Be Prepared to Discuss Code: If you present code, be ready to walk through it and explain your logic.

• Tailor to the Role: Emphasize aspects of your projects that align with data analytics, predictive modeling, and AI agent development.

Challenge Preparation:

• Coding Practice: Brush up on Python fundamentals, data structures, and common libraries (e.g., Pandas, NumPy). Practice SQL queries.

• AI/LLM Concepts: Be ready to discuss basic concepts of LLMs, how they work, and potential applications.

• Problem-Solving Scenarios: Think about how you would approach a data analysis or AI-related problem in a banking context.

📝 Enhancement Note: The application process emphasizes practical demonstration of skills through projects and discussions, suitable for a student role. The motivational letter is a key component for students to articulate their passion and fit.

🛠 Tools & Technology Stack

Primary Tools:

• Python: The core programming language for data analysis, scripting, and model development.

• SQL: Essential for data extraction, manipulation, and database interaction.

• Azure Databricks: A collaborative, cloud-based platform for data engineering, data science, and machine learning.

• Azure Cloud Services: General familiarity with Azure's ecosystem for cloud computing and AI services.

Analytics & Reporting:

• Data Visualization Libraries/Tools: Likely includes Python libraries (e.g., Matplotlib, Seaborn, Plotly) or integrated tools within Azure Databricks for creating reports and dashboards.

• Business Analysis Tools: Standard office productivity software and potentially specialized BI tools.

CRM & Automation:

• AI Agents & LLMs: Direct involvement in developing or utilizing AI agents and large language models.

• Potential Integration Tools: Awareness of how data and AI solutions might integrate with existing banking systems, though direct experience is not expected.

📝 Enhancement Note: The technology stack clearly points towards a modern, cloud-native data science environment, with a strong emphasis on Azure. Proficiency or familiarity with these tools is a significant advantage.

👥 Team Culture & Values

Operations Values:

• Reliability and Responsibility: Taking ownership of tasks and ensuring accuracy in data analysis and model development.

• Ethical Conduct: Upholding high ethical standards, especially crucial in the financial sector and with AI applications.

• Proactiveness/Initiative: Demonstrating a willingness to learn, explore new ideas, and contribute beyond assigned tasks.

• Collaboration: Working effectively with team members and other departments to achieve common goals.

• Customer Orientation: Understanding how data and AI solutions can ultimately benefit customers and the bank's service delivery.

• Sustainability: Contributing to an organization that values environmental and social responsibility.

Collaboration Style:

• Open communication and knowledge sharing within the data analytics and AI teams.

• Cross-functional teamwork to gather requirements and present findings to stakeholders.

• A culture of mentorship and continuous learning, fostering an environment where students can ask questions and grow.

📝 Enhancement Note: The company culture emphasizes ethical responsibility, collaboration, and a forward-thinking approach to business and technology, aligning well with the operations focus of data integrity and efficient solution deployment.

⚡ Challenges & Growth Opportunities

Challenges:

• Bridging Academic Knowledge and Business Application: Translating theoretical concepts learned in university into practical solutions for complex banking challenges.

• Data Complexity and Scale: Working with large, diverse datasets within a regulated financial environment.

• Rapidly Evolving AI Landscape: Staying abreast of the latest advancements in AI and LLMs and understanding their applicability.

• Navigating Corporate Structures: Understanding how to effectively communicate and integrate solutions within a large organization.

Learning & Development Opportunities:

• Hands-on Project Experience: Gaining practical skills in data analysis, Python, SQL, and AI development.

• Mentorship and Guidance: Learning from experienced professionals in a supportive environment.

• Industry-Specific Knowledge: Developing an understanding of the financial services industry and its data needs.

• Exposure to Cloud Technologies: Building proficiency with Azure and Azure Databricks.

📝 Enhancement Note: The challenges presented are typical for students transitioning into professional roles, highlighting the learning curve and the opportunity for significant personal and professional growth.

💡 Interview Preparation

Strategy Questions:

• "Tell me about a time you used Python to solve a data-related problem." (Focus on your process, challenges, and outcome.)

• "How would you approach preparing a business analysis for a new feature recommendation?" (Think about data sources, key metrics, and presentation methods.)

• "Describe your understanding of Large Language Models and how they might be applied in a banking context." (Showcase your knowledge and creative thinking.)

• "What are the key considerations when developing predictive models?" (Discuss data quality, feature engineering, validation, and potential biases.) Company & Culture Questions:

• "Why are you interested in data analytics and AI at NLB, specifically?" (Connect your skills and interests to the company's mission and the role's responsibilities.)

• "How do you approach collaboration with team members, especially when you have different ideas?" (Highlight your teamwork and communication skills.)

• "What does 'customer orientation' mean to you in the context of data analysis?" (Relate your work to customer value and service improvement.) Portfolio Presentation Strategy:

• Storytelling: Frame your projects as narratives – the problem, your solution, and the impact.

• Visual Aids: Use clear, concise slides or demos to illustrate your work. Ensure visualizations are easy to understand.

• Technical Depth: Be prepared to discuss the technical details of your code and methodologies.

• Enthusiasm and Curiosity: Show genuine interest in the field and a desire to learn.

📝 Enhancement Note: Interview preparation should focus on demonstrating practical application of skills, understanding of core concepts, and alignment with NLB's values. The portfolio is a critical tool for showcasing capabilities.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided link on NLB's careers site.

• Tailor your CV: Highlight specific academic projects, programming skills (Python, SQL), and any exposure to AI/LLMs or Azure. Use keywords from the job description.

• Craft a strong motivational letter: Articulate your passion for data analytics and AI, explain why you're interested in NLB and the banking sector, and how this role aligns with your career aspirations.

• Prepare your portfolio: Gather examples of relevant academic or personal projects. Be ready to discuss them in detail, focusing on your process, tools, and outcomes.

• Research NLB: Understand the company's mission, values, and its role in the SEE region. Familiarize yourself with their commitment to sustainability and employee development.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

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