Specialist za podatkovno znanost in UI / Specialist Data Science & AI

📍 Job Overview

Job Title: Specialist Data Science & AI (Specialist za podatkovno znanost in UI)

Company: Novartis

Location: Ljubljana, Slovenia

Job Type: Full time, Temporary (2-year fixed-term contract)

Category: Data Science & Artificial Intelligence / Operations Technology

Date Posted: August 18, 2026

Experience Level: 1+ years

Remote Status: Hybrid

🚀 Role Summary

• Drive the adoption of Agentic and AI capabilities across site and global TechOps processes, embedding AI-enabled ways of working to achieve measurable productivity and efficiency gains.

• Develop AI fluency within IT/OT and business teams, establishing and leading Agentic/AI champion networks to foster hands-on development of no-code/low-code solutions.

• Lead bottom-up identification and prioritization of high-value use cases, overseeing the delivery, scaling, and sustained adoption of Agentic and AI solutions.

• Act as a liaison between local site needs and global product development for Agentic & AI platforms, ensuring continuous improvement and alignment.

• Establish clear governance, track benefits, and report on productivity and FTE impact, maintaining a multi-year roadmap for Agentic & AI advancements.

📝 Enhancement Note: This role is focused on operationalizing AI and Agentic capabilities within a large, established pharmaceutical company. The emphasis is on practical application, driving adoption, and demonstrating tangible business value (productivity, efficiency, FTE impact) rather than pure research. The "TechOps" designation suggests a focus on technology operations within the manufacturing or R&D environments. The specific mention of Microsoft Copilot and Power Platform indicates a strong preference for Microsoft's AI ecosystem.

📈 Primary Responsibilities

• Spearhead the end-to-end adoption of Agentic and AI capabilities across local and global TechOps processes, integrating AI-enabled methodologies into daily operations to deliver measurable productivity improvements.

• Cultivate AI literacy across IT/OT and business departments, establishing and leading communities of Agentic/AI champions to enable practical, hands-on development of no-code/low-code agentic solutions.

• Lead the identification of use cases driven by bottom-up initiatives, collaborating with site leadership to prioritize opportunities, and overseeing the implementation, scaling, and sustained utilization of Agentic and AI solutions.

• Champion the adoption of globally available Agentic and AI platforms while consistently communicating local requirements, identified gaps, and enhancement opportunities to global product teams for inclusion in their backlogs.

• Implement and maintain clear governance frameworks, meticulously track benefits, and provide comprehensive reporting, including detailed analysis of productivity and FTE impact, while also maintaining a forward-looking, multi-year Agentic & AI development roadmap.

• Collaborate closely with Global DDIT Ops and DSAI teams to ensure the readiness of data, platforms, integrations, and security measures, thereby enabling enhanced data self-service capabilities for all sites.

• Lead critical change management initiatives and foster cultural transformation to ensure employees readily embrace AI as a tool for augmenting their work and actively leverage Agentic and AI solutions in their day-to-day responsibilities.

📝 Enhancement Note: The responsibilities highlight a blend of strategic thinking (roadmap, governance) and hands-on execution (driving adoption, enabling development). The emphasis on "bottom-up use case identification" suggests a need for strong stakeholder engagement and the ability to champion new ideas from the ground level. The requirement to "feed site requirements... into global product backlogs" indicates a role that bridges local operational needs with global technology strategy.

🎓 Skills & Qualifications

Education: While not explicitly stated, a Bachelor's or Master's degree in Computer Science, Data Science, Information Technology, Engineering, or a related quantitative field is typically expected for roles involving Data Science and AI.

Experience: Minimum of 1 year of hands-on experience in Data Science and Artificial Intelligence.

Required Skills:

• A minimum of 1 year of experience in Data Science and AI, coupled with a strong interest and curiosity in developing AI solutions.

• Practical, hands-on experience with enterprise AI assistants, with a strong preference for Microsoft Copilot (including M365 Copilot and Copilot Studio).

• A demonstrable and strong interest in automation and robotics.

• Proven ability to develop agentic solutions utilizing no-code/low-code platforms (e.g., Microsoft Power Platform, Copilot Studio), encompassing prompt engineering, workflow design, and basic integration capabilities.

• Practical understanding of agent orchestration principles, the application of reusable patterns, and the scaling of agents within operational environments.

• Foundational knowledge in data science and analytics, including the ability to define Key Performance Indicators (KPIs), perform descriptive analysis, possess SQL proficiency, and interpret AI output effectively.

• Exceptional communication skills, a proactive and "can-do" attitude, and a deeply curious approach, with the ability to delve into business needs and translate them into practical AI solutions. Preferred Skills:

• Familiarity with pharmaceutical processes and the pharmaceutical industry.

• Experience with data fundamentals: capability to consume, combine, and interpret data from enterprise systems (e.g., MES, ERP, LIMS); a basic understanding of data quality, data access, and data governance principles.

• Experience with Python programming.

• Hands-on experience with automation and robotics.

📝 Enhancement Note: The "1+ years" experience requirement suggests this role is suitable for early-career professionals who have some practical exposure, potentially from internships, academic projects, or a prior junior role. The emphasis on Microsoft Copilot and Power Platform is a critical differentiator for candidates. The inclusion of "SQL knowledge" and "descriptive analysis" points towards a need for foundational data analysis skills beyond just AI model deployment.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase of practical AI solutions developed using no-code/low-code platforms (e.g., Power Platform, Copilot Studio), demonstrating prompt engineering, workflow design, and integration capabilities.

• Examples of agent orchestration and the application of reusable patterns in operational settings.

• Case studies illustrating the translation of business needs into AI-driven solutions with measurable outcomes.

• Documentation of basic data analysis, including KPI definition and descriptive analysis, and interpretation of AI results. Process Documentation:

• Examples of how you have documented AI solution development processes, including prompt engineering, workflow design, and integration steps.

• Demonstrations of how you have tracked and reported on the benefits and impact (e.g., productivity, FTE savings) of implemented AI solutions.

• Evidence of contributing to or maintaining a roadmap for AI/Agentic solution development.

📝 Enhancement Note: While a formal "portfolio" might not be explicitly requested in the application, candidates should be prepared to discuss and showcase past projects that align with the requirements. This could include examples from previous roles, academic work, or personal projects, particularly those involving Microsoft Copilot, Power Platform, or similar no-code/low-code AI tools. The ability to articulate the process of problem identification, solution development, and impact measurement will be crucial.

💵 Compensation & Benefits

Salary Range: €26,600 - €49,400 annually (gross).

Benefits:

• Comprehensive insurance plans.

• Robust retirement programs.

• Access to wellbeing resources.

• Global recognition programs.

• Flexible working options.

• Hybrid working options.

• Minimum of 14 weeks paid parental leave.

Working Hours: While not explicitly stated, the standard full-time work week is assumed to be approximately 40 hours. The hybrid work arrangement indicates flexibility in how these hours are managed.

📝 Enhancement Note: The salary range provided is an expected annual base gross salary. The actual offer will depend on the candidate's specific skills, experience, and competencies. The benefits package is competitive and comprehensive, reflecting Novartis' commitment to employee well-being and work-life balance, with specific mentions of parental leave and flexible/hybrid work options that are attractive to operations professionals.

🎯 Team & Company Context

🏢 Company Culture

Industry: Pharmaceutical and Biotechnology. Novartis is a global leader in healthcare, focused on discovering, developing, manufacturing, and marketing innovative medicines. This industry context means a strong emphasis on quality, compliance, data integrity, and patient outcomes.

Company Size: Novartis is a large multinational corporation with tens of thousands of employees worldwide. This scale implies robust processes, significant resources, and opportunities for cross-functional collaboration and career development, but also potentially more complex internal structures.

Founded: Novartis was formed in 1996 through the merger of Ciba-Geigy and Sandoz. This long history in the pharmaceutical sector provides a foundation of expertise and established operational frameworks.

Team Structure:

• The role likely sits within a broader IT Operations (IT/OT) or Digital Technology & Innovation (DDIT) department, potentially part of a dedicated Data Science & AI (DSAI) or Digital Transformation team.

• Collaboration is expected with various business teams, IT/OT personnel, and global DSAI/Ops teams.

• The structure likely involves reporting to a manager overseeing AI/Digital initiatives, with a matrixed relationship to site leadership for use case prioritization and adoption. Methodology:

• Emphasis on data-driven decision-making and leveraging AI for tangible business outcomes.

• A methodology that supports both top-down strategic initiatives and bottom-up innovation from employees.

• Collaboration with global teams to ensure scalability and consistency of AI solutions.

• A strong focus on change management and fostering a culture that embraces new technologies like AI.

Company Website: https://www.novartis.com/

📝 Enhancement Note: The pharmaceutical industry context is crucial. Operations roles within Novartis will operate under strict regulatory guidelines (e.g., Good Manufacturing Practices - GMP) and require a high degree of data integrity and process rigor. The "TechOps" aspect likely refers to technology operations within manufacturing, R&D, or supply chain functions.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a Specialist, indicating an individual contributor role requiring specific technical expertise and practical application skills. It's an entry to mid-level position suitable for those with foundational experience looking to deepen their expertise in AI operationalization.

Reporting Structure: The Specialist will likely report to an IT Operations Manager or a Digital Transformation Lead. They will collaborate closely with site leadership for use case prioritization and global DDIT Ops and DSAI teams for platform alignment and support.

Operations Impact: The primary impact of this role is on driving significant productivity and efficiency gains within TechOps processes through the strategic implementation of Agentic and AI capabilities. This includes improving operational workflows, automating tasks, and enabling data self-service, ultimately contributing to cost savings and enhanced operational performance.

Growth Opportunities:

• Skill Specialization: Deepen expertise in specific AI tools and platforms, particularly within the Microsoft ecosystem (Copilot, Power Platform), and gain advanced knowledge in prompt engineering and agent orchestration.

• Cross-functional Leadership: Develop skills in change management, stakeholder engagement, and leading AI champion networks, potentially leading to roles with more strategic oversight or team leadership.

• Industry Expertise: Gain specialized knowledge of pharmaceutical manufacturing and R&D processes, becoming a subject matter expert in applying AI within this regulated industry.

• Global Collaboration: Transition to roles with broader global responsibilities, contributing to the development of global AI strategies and platforms.

📝 Enhancement Note: The role offers a clear path for growth for individuals passionate about AI and its practical application in an industrial setting. The emphasis on "no-code/low-code" suggests a focus on democratizing AI development, which is a growing trend. The temporary contract nature (2 years) means candidates should be aware of this, but it can also be an opportunity to gain significant experience within a leading pharmaceutical company.

🌐 Work Environment

Office Type: The role is designated as Hybrid, indicating a blend of remote work and on-site presence at the Ljubljana office. This offers a balance between flexibility and in-person collaboration.

Office Location(s): Ljubljana, Slovenia. Novartis has a significant presence in Slovenia, and the Ljubljana office likely serves as a hub for various business and technology functions.

Workspace Context:

• Collaborative Environment: The hybrid model and emphasis on building AI fluency and champion networks suggest a collaborative workspace where team members share knowledge and work together on AI initiatives.

• Technology Access: Employees will have access to the necessary IT infrastructure, software, and potentially specialized hardware to develop and deploy AI solutions. This includes access to enterprise AI platforms like Microsoft Copilot and the Power Platform.

• Cross-functional Interaction: Opportunities to interact with diverse teams across IT/OT, business units, and global functions, fostering a rich learning environment.

Work Schedule: Standard full-time hours are expected, with flexibility provided through the hybrid work arrangement. This allows for better management of personal and professional commitments while ensuring operational needs are met.

📝 Enhancement Note: The hybrid model is a key aspect of the work environment, offering flexibility. Candidates should confirm the specific expectations regarding days in the office versus remote work directly with the hiring team. The Ljubljana location is fixed, and relocation support is not provided, making local accessibility a prerequisite.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A review of applications based on essential requirements, focusing on AI/Data Science experience, Copilot/Power Platform knowledge, and communication skills.

• Technical/Behavioral Interview: Discussion of past projects, practical experience with AI tools, problem-solving approach, and understanding of business needs translation into AI solutions. Expect questions on prompt engineering, workflow design, and basic data analysis.

• Case Study/Challenge: Potentially a practical exercise or case study to assess the ability to identify a use case, propose an AI solution using no-code/low-code tools, and articulate its potential impact.

• Team/Cultural Fit Interview: Assessment of collaboration style, proactive attitude, curiosity, and alignment with Novartis' values and the team's working methods.

Portfolio Review Tips:

• Showcase No-Code/Low-Code AI: Prepare examples of AI solutions built with tools like Power Platform or Copilot Studio. Focus on the problem solved, the workflow designed, and the outcomes achieved.

• Demonstrate Prompt Engineering: Be ready to discuss your approach to crafting effective prompts for AI models.

• Quantify Impact: Whenever possible, quantify the benefits of your projects (e.g., time saved, efficiency gained, errors reduced).

• Business Acumen: Highlight your ability to understand business challenges and translate them into technical AI requirements.

• Process Documentation: Be prepared to discuss how you would document AI solutions for scalability and maintenance.

Challenge Preparation:

• Use Case Identification: Practice identifying potential AI use cases within a corporate or industrial setting, focusing on areas where productivity or efficiency can be improved.

• Solution Design: Think through how you would design a no-code/low-code AI solution for a given problem, considering data sources, workflow logic, and user interaction.

• Impact Assessment: Prepare to articulate the potential ROI and operational impact of your proposed solutions.

• Change Management: Consider how you would approach introducing new AI tools to a workforce and managing the associated change.

📝 Enhancement Note: Given the "Specialist" level and focus on no-code/low-code, the interview process will likely emphasize practical application and problem-solving over deep theoretical knowledge. Candidates should be prepared to "show, not just tell" their capabilities. The ability to articulate the "why" behind an AI solution and its business value will be as important as the technical "how."

🛠 Tools & Technology Stack

Primary Tools:

• Microsoft Copilot: Essential for enterprise AI assistant capabilities (M365 Copilot, Copilot Studio).

• Microsoft Power Platform: Key for developing no-code/low-code solutions (Power Apps, Power Automate, Power BI).

• Agentic AI Platforms: Broader understanding of platforms that facilitate the creation and deployment of AI agents.

Analytics & Reporting:

• SQL: For basic data querying and analysis.

• Data Analytics Tools: Proficiency in interpreting AI outputs and performing descriptive analysis.

• Power BI: Likely used for dashboard creation and reporting, especially when integrated with Power Platform.

CRM & Automation:

• Process Automation Tools: Beyond Power Automate, familiarity with general automation principles.

• Enterprise Systems Integration: Understanding how AI solutions can integrate with systems like MES, ERP, and LIMS.

📝 Enhancement Note: The technology stack is heavily weighted towards the Microsoft ecosystem, particularly M365 Copilot and Power Platform. Candidates with direct experience in these tools will have a significant advantage. Familiarity with enterprise systems like MES, ERP, and LIMS is beneficial for understanding the operational context.

👥 Team Culture & Values

Operations Values:

• Innovation & Efficiency: A drive to leverage new technologies like AI to improve operational processes and achieve measurable gains in productivity and efficiency.

• Collaboration & Knowledge Sharing: Emphasis on building AI fluency across teams, establishing champion networks, and sharing best practices for AI adoption.

• Data-Driven Approach: Utilizing data to identify opportunities, measure impact, and guide the development and deployment of AI solutions.

• Customer Focus (Internal/External): Understanding and translating the needs of internal business teams into effective AI solutions that enhance their work.

• Continuous Improvement: A commitment to evolving AI capabilities, iterating on solutions, and staying abreast of technological advancements.

Collaboration Style:

• Cross-functional Integration: Working seamlessly with IT/OT, business units, and global digital teams to ensure AI solutions are well-integrated and adopted.

• Empowerment & Enablement: Empowering employees through AI fluency training and providing tools for hands-on development, fostering a culture of innovation.

• Feedback Loops: Establishing mechanisms for continuous feedback between local sites and global product teams to refine AI platforms and solutions.

• Proactive Communication: Maintaining open and clear communication channels to manage expectations, report progress, and address challenges related to AI implementation.

📝 Enhancement Note: Novartis, like many large pharmaceutical companies, likely values a culture that balances innovation with a strong adherence to quality and compliance. The emphasis on "Agentic & AI capabilities" and "AI fluency" suggests a forward-thinking approach to technology adoption within a structured environment.

⚡ Challenges & Growth Opportunities

Challenges:

• Driving Adoption: Overcoming resistance to change and ensuring widespread adoption of new AI tools and ways of working across diverse user groups.

• Scalability: Successfully scaling no-code/low-code AI solutions from pilot phases to enterprise-wide deployment while maintaining governance and performance.

• Data Readiness: Ensuring data quality, accessibility, and integration across various enterprise systems to support AI initiatives.

• Bridging Local & Global: Effectively managing the flow of information and requirements between local site needs and global platform development roadmaps.

• Measuring ROI: Clearly defining and consistently tracking the productivity and FTE impact of AI solutions to demonstrate value to stakeholders.

Learning & Development Opportunities:

• AI Specialization: Gaining deep expertise in enterprise AI assistants, prompt engineering, and agent orchestration within the Microsoft ecosystem.

• No-Code/Low-Code Mastery: Becoming a power user and developer within the Microsoft Power Platform for AI-driven solutions.

• Industry Insight: Developing a strong understanding of pharmaceutical TechOps processes and how AI can optimize them.

• Change Management Skills: Enhancing capabilities in leading cultural shifts and driving user adoption of new technologies.

• Project Management: Managing the lifecycle of AI solutions from ideation through to sustained adoption.

📝 Enhancement Note: The challenges highlight the practical realities of implementing AI in a large organization, emphasizing the importance of change management, stakeholder alignment, and quantifiable results. The growth opportunities are well-defined, offering a clear development path for individuals focused on operationalizing AI.

💡 Interview Preparation

Strategy Questions:

• "Describe a situation where you translated a complex business need into a functional AI solution using no-code/low-code tools. What was your process, and what was the outcome?" (Focus on problem identification, solution design, prompt engineering, and impact measurement).

• "How would you approach building AI fluency within a team that has limited technical expertise? What strategies would you use to encourage adoption of tools like Microsoft Copilot?" (Focus on change management, training, and championing).

• "Imagine you've identified a promising AI use case at a site. How would you go about prioritizing it, securing buy-in from leadership, and planning for its implementation and scaling?" (Focus on business acumen, prioritization, and project planning). Company & Culture Questions:

• "What interests you most about applying AI within the pharmaceutical industry, specifically at Novartis?" (Demonstrate research into Novartis and the industry).

• "How do you see AI augmenting the work of IT/OT and business professionals, rather than replacing them?" (Align with the company's vision of AI as an augmentation tool).

• "Describe a time you had to collaborate with teams from different functional areas or geographies. How did you ensure effective communication and alignment?" (Highlight cross-functional collaboration skills). Portfolio Presentation Strategy:

• Structure Your Examples: For each project, clearly articulate: 1) The Business Problem, 2) The AI Solution (mentioning specific tools like Copilot/Power Platform), 3) Your Role/Contribution, 4) The Results/Impact (quantified if possible).

• Focus on Process: Explain your thought process for designing prompts, building workflows, and integrating solutions.

• Demonstrate Understanding: Show your grasp of how the AI solution addresses the specific business need and contributes to operational efficiency.

• Keep it Concise: Be prepared to present key highlights efficiently, allowing time for Q&A.

📝 Enhancement Note: Interview preparation should focus on demonstrating practical skills with specific AI tools, a strong understanding of business needs, and effective communication. Candidates should be ready to discuss their experience with Microsoft Copilot and Power Platform in detail and articulate the business value they can bring.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided link on the Novartis careers portal.

• Tailor your CV: Ensure your CV highlights your experience in Data Science, AI, no-code/low-code development (especially Microsoft Copilot and Power Platform), automation, and any relevant industry experience. Use keywords from the job description.

• Prepare your portfolio: Gather examples of projects or use cases you've worked on that demonstrate your ability to build AI solutions using no-code/low-code tools and articulate their business impact. Be ready to discuss these in detail during interviews.

• Research Novartis and the Pharma Industry: Understand Novartis' mission, values, and the specific challenges and opportunities within the pharmaceutical sector concerning AI adoption.

• Practice your pitch: Be ready to clearly articulate your interest in the role, your relevant skills, and how you can contribute to driving AI adoption within TechOps.

⚠️ 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.