Specialist za podatkovno znanost in UI (m/d/f) / Specialist Data Science & AI (m/d/f)

📍 Job Overview

Job Title: Specialist za podatkovno znanost in UI (m/d/f) / Specialist Data Science & AI (m/d/f)

Company: Novartis

Location: Ljubljana, Slovenia

Job Type: Full-time

Category: Data Science & AI / Technology Operations

Date Posted: May 12, 2026

Experience Level: Mid-level (2-5 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 deliver measurable productivity gains.

• Build AI fluency across IT/OT and business teams, establishing and leading Agentic/AI champions to enable hands-on development of no-code/low-code agentic solutions.

• Lead bottom-up use case identification, prioritize high-value opportunities with site leadership, and oversee the delivery, scaling, and sustained adoption of Agentic & AI solutions.

• Foster adoption of globally available Agentic & AI platforms while continuously feeding site requirements, gaps, and enhancement opportunities into global product backlogs.

📝 Enhancement Note: This role is positioned at a mid-level (Specialist) within the Data Science & AI domain, focusing on practical application and adoption of AI technologies within TechOps. The emphasis on "Agentic AI" and "no-code/low-code" solutions indicates a strong focus on empowering non-technical users and streamlining processes through AI, aligning with modern GTM and operational efficiency trends. The hybrid work arrangement suggests a need for strong self-management and communication skills.

📈 Primary Responsibilities

• Spearhead the end-to-end adoption of Agentic & AI capabilities across local and global TechOps processes, integrating AI-enabled workflows into daily operations to achieve significant productivity improvements.

• Develop AI fluency within IT/OT and business teams, establishing a network of Agentic/AI champions and facilitating practical, hands-on development of no-code/low-code agentic solutions.

• Lead the identification of use cases driven by bottom-up initiatives, collaborate with site leadership for prioritization, and manage the implementation, scaling, and ongoing adoption of Agentic & AI solutions.

• Champion the use of globally available Agentic & AI platforms, ensuring local requirements, identified gaps, and enhancement opportunities are effectively communicated to global product development teams.

• Establish robust governance frameworks, track and report on key benefits (including productivity and FTE impact), and maintain a forward-looking, multi-year roadmap for Agentic & AI development.

• Collaborate closely with Global DDIT Ops and DSAI teams to ensure data, platform, integration, and security readiness, thereby enabling data self-service capabilities for various sites.

• Lead change management initiatives and foster cultural transformation to ensure employees embrace AI as an augmentation tool and actively leverage Agentic & AI solutions in their daily work.

📝 Enhancement Note: The responsibilities highlight a blend of strategic oversight (roadmap, governance, prioritization) and hands-on execution (solution development, AI fluency building). The focus on "bottom-up use case identification" and "change management" indicates that influencing stakeholders and driving adoption will be as critical as technical expertise. The need to translate "local requirements" into "global product backlogs" suggests a crucial liaison role.

🎓 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 this level of specialization.

Experience: Minimum of 2 years of direct experience in Data Science and Artificial Intelligence, with a demonstrable passion for AI solution development.

Required Skills:

• Minimum 2 years of experience in Data Science and AI.

• Strong interest and curiosity in AI solution development.

• Hands-on experience with Microsoft Copilot (M365 Copilot, Copilot Studio) and other enterprise AI assistants.

• Proficiency in Python programming.

• Familiarity with automation and robotics concepts and applications.

• Proven ability to build agentic solutions using no-code/low-code platforms (e.g., Microsoft Power Platform, Copilot Studio).

• Expertise in prompt engineering, workflow design, and basic integrations within AI solutions.

• Practical understanding of agent orchestration, reusable patterns, and scaling agents in operational environments.

• Foundational data science and analytics skills, including KPI definition, descriptive analysis, and SQL knowledge.

• Ability to interpret AI outputs and translate them into actionable insights.

• Excellent communication and interpersonal skills.

Preferred Skills:

• Knowledge of the pharmaceutical process and industry.

• Experience with data fundamentals, including consuming, combining, and interpreting data from enterprise systems (e.g., MES, ERP, LIMS).

• Basic understanding of data quality, data access, and data governance principles.

📝 Enhancement Note: The requirements emphasize practical, hands-on experience with specific AI tools like Microsoft Copilot and the Power Platform, alongside foundational programming skills in Python and database querying with SQL. The "no-code/low-code" focus is a key differentiator, suggesting the role is about democratizing AI rather than solely deep algorithmic development. The preference for pharmaceutical industry knowledge is a significant plus for understanding the operational context.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrations of Agentic/AI solutions built using no-code/low-code platforms (e.g., Power Platform, Copilot Studio), showcasing prompt engineering, workflow design, and integration capabilities.

• Case studies detailing the identification, development, and implementation of AI use cases, highlighting measurable productivity gains or efficiency improvements.

• Examples of how you've translated complex business needs into practical AI-driven solutions.

Process Documentation:

• Documented examples of AI fluency building initiatives or community leadership (e.g., champion networks, training materials).

• Workflows illustrating the process of identifying, prioritizing, and scaling AI solutions within an organizational context.

• Examples of how you've tracked and reported on the benefits and impact of AI implementations, including productivity and FTE metrics.

• Evidence of contributions to or maintenance of AI roadmaps and governance frameworks.

📝 Enhancement Note: Candidates should prepare to showcase tangible projects that demonstrate their ability to build and deploy AI solutions using specified tools. The portfolio should not only highlight technical skills but also the ability to drive adoption, manage change, and quantify business impact, aligning with typical Revenue Operations and GTM efficiency metrics.

💵 Compensation & Benefits

Salary Range: For a Specialist Data Science & AI role with 2-5 years of experience in Ljubljana, Slovenia, the estimated annual gross salary range typically falls between €35,000 and €55,000. This is an estimate based on industry benchmarks for similar roles in Slovenia, considering the company's size and the specific technical skills required.

• Methodology: This estimate is derived from analyzing compensation data for Data Scientists, AI Specialists, and IT Operations roles in the Ljubljana region, factoring in experience level, and the specialized nature of AI and no-code/low-code development. Data sources include regional salary surveys and industry compensation reports.

Benefits:

• Competitive salary package.

• Annual bonus.

• Flexible working schedule, tailored to individual needs.

• Option to work from home (hybrid arrangement).

• Pension scheme.

• Possibility of joining a collective health insurance scheme.

• Employee Recognition Scheme.

• Expanded program for the promotion of health in the field of physical and mental well-being and workload management (Well-being).

• Unlimited learning and development opportunities.

Working Hours: The role is full-time, implying approximately 40 hours per week. The "Hybrid" work arrangement and "Flexible working schedule" indicate potential for adjusted daily start/end times and the ability to work remotely, subject to operational needs and team coordination, particularly for AI implementation and support.

📝 Enhancement Note: The salary estimate is based on market data for Ljubljana, Slovenia. The benefits package is comprehensive, emphasizing work-life balance, professional development, and employee well-being, which are attractive to operations professionals seeking stability and growth. The flexibility offered in working hours and location, within a hybrid model, is a key consideration for candidates.

🎯 Team & Company Context

🏢 Company Culture

Industry: Pharmaceutical and Biotechnology. Novartis is a global leader in discovering, developing, manufacturing, and marketing innovative medicines. This industry context implies a highly regulated environment, a strong focus on R&D, and a mission-driven culture aimed at improving patient lives. For operations roles, this means a focus on efficiency, compliance, data integrity, and process optimization to support drug development and manufacturing.

Company Size: Novartis is a large, multinational corporation with tens of thousands of employees globally. A large company structure often means well-defined processes, opportunities for specialization, and a structured career path, but also a need for individuals who can navigate complex organizational landscapes and drive change within established frameworks.

Founded: 1996, through the merger of Ciba-Geigy and Sandoz. This history suggests a company with deep roots in the life sciences, combining legacy expertise with a forward-looking approach to innovation.

Team Structure:

• The role is within TechOps (Technology Operations), likely a division of IT or a specialized operations unit supporting global processes.

• It involves collaboration with global DDIT Ops (Digital and IT Operations) and DSAI (Data Science & AI) teams, indicating a matrixed reporting structure or strong cross-functional project teams.

Methodology:

• Data-Driven Decision Making: The role inherently requires a strong reliance on data science, analytics, and AI outputs to identify opportunities and measure impact.

• Process Optimization & Efficiency: The core objective is to drive productivity and efficiency gains through AI adoption, underscoring a commitment to continuous improvement.

• Agile & Iterative Development: The mention of "bottom-up use case identification" and "global product backlogs" suggests an agile approach to solution development and deployment, allowing for rapid iteration and adaptation.

• Collaboration & Knowledge Sharing: The establishment of AI communities and the need to feed local requirements into global backlogs highlight a collaborative, knowledge-sharing culture.

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

📝 Enhancement Note: Novartis's scale and industry context mean this role operates within a complex, global environment focused on innovation and patient outcomes. The TechOps team likely plays a critical role in leveraging technology to enhance operational efficiency and digital transformation, making this a high-impact position within a well-established organization.

📈 Career & Growth Analysis

Operations Career Level: Specialist. This level typically signifies an individual contributor role requiring specialized knowledge and skills, with a focus on executing defined projects and initiatives. It's a step beyond entry-level, demanding practical experience and the ability to work with some autonomy, but still operating within established frameworks and under the guidance of senior team members or managers. For this role, it means being a go-to person for AI adoption and no-code/low-code solutions within TechOps.

Reporting Structure: The role involves collaboration with global DDIT Ops and DSAI teams, suggesting a reporting line likely within a local TechOps or IT function, with strong dotted-line connections to global AI and operations leads. This structure allows for both local operational focus and alignment with global strategies.

Operations Impact: The Specialist will directly impact operational efficiency and productivity by driving the adoption of AI technologies. This includes:

• Productivity Gains: Enabling measurable improvements in task completion times and output volume.

• Efficiency Improvements: Streamlining processes and reducing manual effort through automation and AI assistance.

• Digital Transformation: Contributing to the broader digital transformation agenda within TechOps by embedding AI into daily workflows.

• Cost Savings: Potential FTE savings or reallocation of resources to higher-value activities through AI-driven efficiencies.

Growth Opportunities:

• Specialization: Deepen expertise in Agentic AI, specific AI platforms (Copilot, Power Platform), and prompt engineering.

• Project Leadership: Lead cross-functional projects for AI solution deployment and adoption.

• Technical Advancement: Transition into more senior technical roles (e.g., Senior Data Scientist, AI Architect) or roles focused on AI strategy and governance.

• Managerial Track: Potentially move into team leadership or management roles within TechOps or Data Science, overseeing AI initiatives and teams.

• Cross-Functional Exposure: Gain broad experience across different business units and global operations through collaboration with various teams.

📝 Enhancement Note: This Specialist role offers a solid foundation for a career in AI and operational technology. The emphasis on "AI fluency" and "no-code/low-code" solutions presents a unique opportunity to develop skills in democratizing AI, which is a growing area. Growth paths are clear, either deepening technical expertise or moving into more strategic or leadership roles within a large, reputable organization like Novartis.

🌐 Work Environment

Office Type: The role is designated as Hybrid. This implies a mix of working from the company's office in Ljubljana and working remotely. The "flexible working schedule" and "possibility to work from home" further support this arrangement.

Office Location(s): Ljubljana, Slovenia. This location offers a vibrant European capital setting with access to talent and a growing tech ecosystem.

Workspace Context:

• Collaborative Environment: The hybrid model and emphasis on building AI communities suggest active collaboration with colleagues both in person and virtually. Expect team meetings, brainstorming sessions, and cross-functional project work.

• Technology Access: As a Specialist in Data Science & AI, access to modern computing resources, relevant software licenses (Microsoft 365, Power Platform, Python environments), and robust network infrastructure is expected.

• Team Interaction: Regular interaction with IT/OT teams, business stakeholders, and global DSAI/DDIT Ops colleagues will be essential for driving AI adoption and gathering requirements.

Work Schedule: Full-time, with a flexible schedule. This allows for adaptation of daily working hours to personal needs, provided operational demands and team collaboration requirements are met. Standard working hours are likely around 8 hours per day, 5 days a week, but the flexibility aspect is a key benefit.

📝 Enhancement Note: The hybrid nature of the role, combined with flexibility, suggests a modern work environment that values autonomy and work-life balance. Candidates should be comfortable with both independent remote work and in-office collaboration, demonstrating strong self-management and communication skills.

📄 Application & Portfolio Review Process

Interview Process: While not explicitly detailed, a typical process for such a role at a large organization like Novartis would likely include:

• Initial Screening: HR or recruiter call to assess basic qualifications, interest, and cultural fit.

• Technical Interview(s): In-depth discussions with hiring managers and/or senior team members focusing on technical skills, problem-solving abilities, and experience with AI tools, Python, SQL, and no-code/low-code platforms. This may include live coding exercises or scenario-based questions.

• Case Study/Presentation: Candidates may be asked to present a portfolio of work or a prepared case study demonstrating their approach to solving a business problem using AI, or to tackle a take-home challenge.

• Behavioral/Cultural Fit Interview: Assessing alignment with Novartis's values, teamwork capabilities, and how candidates handle challenges and collaboration.

• Final Interview: Possibly with a higher-level manager or department head.

Portfolio Review Tips:

• Showcase No-Code/Low-Code Solutions: Prioritize examples built with Power Platform or Copilot Studio. Detail the specific problem solved, the AI components used (prompts, workflows), and the resulting impact.

• Quantify Impact: For each project, clearly articulate the measurable benefits achieved (e.g., % increase in productivity, hours saved, reduction in errors). Use concrete numbers and metrics.

• Demonstrate AI Fluency Building: Include examples of how you've educated others, led AI champion networks, or facilitated hands-on AI development for non-technical users.

• Highlight Pharmaceutical Context (if applicable): If you have experience in the pharma industry, showcase projects that demonstrate understanding of MES, ERP, LIMS, or pharmaceutical processes.

• Structure Your Presentation: Organize your portfolio logically, perhaps by use case type or technology. Be prepared to walk through your projects, explaining your thought process, technical choices, and outcomes.

Challenge Preparation:

• AI Use Case Brainstorming: Be ready to brainstorm potential AI applications for TechOps or pharmaceutical processes, focusing on prompt engineering and no-code/low-code feasibility.

• Problem-Solving Scenarios: Prepare to discuss how you would approach a specific operational challenge using AI, detailing your steps from understanding the need to implementing and measuring the solution.

• Technical Deep Dive: Brush up on Python fundamentals, SQL querying, and the capabilities of Microsoft Copilot and Power Platform.

📝 Enhancement Note: Candidates should prepare to demonstrate practical AI application skills, particularly in no-code/low-code environments, and their ability to drive adoption and measure impact. A portfolio that clearly articulates business value and showcases collaboration will be highly advantageous.

🛠 Tools & Technology Stack

Primary Tools:

• Microsoft Copilot: M365 Copilot, Copilot Studio for building conversational AI and automated workflows.

• Microsoft Power Platform: Power Automate for workflow automation, Power Apps for building custom applications, and potentially Power BI for data visualization.

• Python: For programming, scripting, and potentially more advanced data analysis or AI model development.

• SQL: For querying and manipulating data from various enterprise systems.

Analytics & Reporting:

• Power BI: Likely used for dashboard creation and reporting, given the Microsoft ecosystem focus.

• Other BI Tools: Depending on global standards, other enterprise BI platforms might be in use.

• AI Output Interpretation: Ability to analyze and interpret results from AI models and tools.

CRM & Automation:

• Enterprise Systems: While not explicitly mentioned as CRM, the role requires integration with and data consumption from systems like MES (Manufacturing Execution System), ERP (Enterprise Resource Planning), and LIMS (Laboratory Information Management System).

• Automation Tools: Beyond Power Automate, understanding of general automation principles and potentially robotics (RPA) is beneficial.

• Integration Platforms: Familiarity with how different systems connect and share data is implied.

📝 Enhancement Note: The technology stack is heavily centered around the Microsoft ecosystem, with a strong emphasis on AI tools like Copilot and automation/low-code platforms like Power Platform. Proficiency in Python and SQL is crucial for data handling and scripting, while understanding enterprise systems like MES and ERP is key for context in a pharmaceutical TechOps environment.

👥 Team Culture & Values

Operations Values:

• Innovation & Improvement: A continuous drive to find better ways of working, leveraging new technologies like AI to enhance processes.

• Collaboration: Working effectively across teams, sharing knowledge, and supporting colleagues to achieve common goals.

• Data-Driven Approach: Making decisions based on data and evidence, and rigorously measuring the impact of initiatives.

• Efficiency & Productivity: A focus on streamlining operations and maximizing output through smart use of technology and processes.

• Patient Focus: Ultimately, all operations within Novartis contribute to bringing innovative medicines to patients, fostering a sense of purpose and responsibility.

Collaboration Style:

• Cross-Functional Integration: Actively engaging with IT, OT, business units, and global AI/Ops teams to ensure AI solutions meet diverse needs and are seamlessly integrated.

• Community Building: Fostering an environment where AI knowledge is shared, and colleagues are empowered to explore and adopt AI tools.

• Feedback Exchange: Encouraging open communication and constructive feedback to refine AI solutions and adoption strategies.

• Proactive Engagement: Taking initiative to identify opportunities, address challenges, and support colleagues in their AI journey.

📝 Enhancement Note: Novartis likely fosters a culture of scientific rigor, innovation, and collaboration. For this role, a proactive, team-oriented approach is essential, as success depends on influencing and enabling others to adopt new AI technologies. The value of "patient focus" provides a strong underlying purpose for operational excellence.

⚡ Challenges & Growth Opportunities

Challenges:

• Driving AI Adoption: Overcoming resistance to change and building AI fluency across diverse teams with varying technical aptitudes.

• Scalability: Ensuring that no-code/low-code solutions can be effectively scaled across different sites and processes while maintaining governance and quality.

• Data Readiness: Working with potentially siloed or inconsistent data across enterprise systems (MES, ERP, LIMS) to enable effective AI applications.

• Integration Complexity: Connecting AI tools with existing legacy systems and ensuring seamless data flow.

• Measuring ROI: Clearly demonstrating the productivity and efficiency gains from AI initiatives to justify investment and continued adoption.

Learning & Development Opportunities:

• AI Specialization: Advanced training in specific AI domains, agent orchestration, and prompt engineering.

• Platform Expertise: Deepening skills in Microsoft Copilot, Power Platform, and related AI tools.

• Pharma Industry Knowledge: Gaining in-depth understanding of pharmaceutical manufacturing, R&D, and supply chain processes.

• Change Management & Leadership: Developing skills in influencing stakeholders, managing organizational change, and potentially leading AI-focused teams.

• Global Exposure: Working on international projects and collaborating with diverse teams across Novartis globally.

📝 Enhancement Note: The primary challenge will be influencing behavior and driving adoption of AI technologies within a large, established organization. The growth opportunities are substantial, allowing for specialization in cutting-edge AI applications and advancement into strategic roles within a leading pharmaceutical company.

💡 Interview Preparation

Strategy Questions:

• "Describe a scenario where you successfully drove the adoption of a new technology (like AI) within a business unit. What were the key challenges, and how did you overcome them?" (Focus on change management, stakeholder engagement, and value proposition).

• "How would you identify and prioritize potential use cases for Agentic AI within a pharmaceutical TechOps environment, considering both technical feasibility and business impact?" (Demonstrate understanding of the industry and process).

Company & Culture Questions:

• "What interests you about Novartis and specifically this role in driving AI adoption in TechOps?" (Connect your passion for AI with Novartis's mission and industry).

• "How do you approach building 'AI fluency' or empowering non-technical users with AI tools?" (Highlight your ability to educate and enable others).

Portfolio Presentation Strategy:

• Structure: Organize your presentation around key projects. For each, clearly define the business problem, your proposed AI solution, the tools/technologies used (emphasizing no-code/low-code), the implementation process, and the quantifiable results (productivity, efficiency gains).

• Focus on Impact: Quantify the benefits achieved. Use metrics like "reduced processing time by X%", "increased output by Y%", or "saved Z FTE hours annually."

• Highlight Collaboration: Explain how you worked with stakeholders, gathered requirements, and managed feedback.

• Demonstrate Technical Aptitude: Be ready to briefly explain the technical aspects of your solutions, especially prompt engineering and workflow logic.

• Tailor to Novartis: If possible, subtly tie your examples to the pharmaceutical industry or large-scale operational environments.

📝 Enhancement Note: Candidates should prepare to articulate their experience in driving AI adoption, demonstrate practical skills with no-code/low-code AI tools, and quantify the business impact of their work. Understanding Novartis's industry and values will be crucial for cultural fit.

📌 Application Steps

To apply for this operations position:

• Submit your application through the Novartis Careers portal link provided.

• Tailor your CV: Ensure your resume highlights experience with Python, SQL, Microsoft Copilot, Power Platform, and any relevant pharmaceutical industry exposure. Use keywords from the job description.

• Prepare your Portfolio: Gather examples of no-code/low-code AI solutions you've built, case studies demonstrating productivity gains, and any experience in enabling AI fluency in others. Be ready to present these.

• Research Novartis: Understand their mission, values, and recent advancements in digital transformation and AI. Familiarize yourself with the pharmaceutical industry context.

• Practice your Pitch: Be ready to articulate your understanding of Agentic AI, your approach to driving adoption, and how your skills align with the role's requirements during interviews.

⚠️ 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.