Data Scientist Degree Apprentice - BSc (Hons)

About Company

Mundipharma is a global healthcare company focussing on customers across Africa, Asia Pacific, Canada, Europe, Latin America, and the Middle East.

Mundipharma is dedicated to bringing innovative treatments to patients in the areas of pain management, infectious disease as well as other severe and debilitating disease areas. Their guiding principles, centred around Integrity and Patient-Centricity, are at the heart of everything they do. For more information visit https://www.mundipharma.com/

Diversity and inclusion

Building an inclusive environment where people can thrive, grow and achieve their full potential is a priority. We believe this isn’t just the right thing, but also the smart thing to do. We are on a journey and will seek to move forward together through education and awareness to build a culture that welcomes and celebrates diversity and uniqueness. We will create a workplace environment where everyone can, every day, bring their authentic selves and is treated with dignity and respect.

Duties and Responsibilities

Data Analysis and Insight

• Gather, clean, transform and explore data from approved supply chain sources.

• Apply descriptive statistics, visualisation and exploratory analysis to identify patterns, trends, anomalies and opportunities.

• Translate analysis into clear findings and practical recommendations for technical and non-technical stakeholders.

• Maintain reproducible analysis and clearly document assumptions, methods and limitations.

Data Science, AI and Machine Learning

• Support the design, development and validation of statistical, predictive, optimisation and machine-learning models under appropriate supervision.

• Use scientific methods, including problem definition, hypothesis development, experiment design, measurement and evaluation.

• Compare alternative approaches using suitable performance measures and explain trade-offs, risks and limitations.

• Contribute to responsible AI use by considering bias, explainability, privacy, security and human oversight.

Data Engineering and Technical Practice

• Use programming and query tools such as Python, SQL and approved analytics platforms to manipulate and analyze data.

• Support the creation of reliable data pipelines, reusable code, dashboards and analytical products.

• Apply development standards including version control, testing, peer review and structured documentation.

• Work with data owners and technical teams to understand data structures, interfaces, lineage and quality issues.

Supply Chain Application

• Apply data science to relevant supply chain challenges such as demand and supply planning, inventory, service, logistics, master data quality and process performance.

• Work with subject matter experts to clarify business needs, define success measures and select realistic analytical approaches.

• Support pilots and proof-of-concept activities, evaluate outcomes and help transition successful solutions into sustainable business use.

• Contribute to continuous improvement initiatives and quantify benefits where evidence allows.

Data Governance, Quality and Compliance

• Handle company, personal and commercially sensitive data ethically and only through approved systems and processes.

• Follow data governance, information security, privacy, retention, quality and access-control requirements.

• Identify data quality issues, investigate root causes and support remediation with data owners and stewards.

• Ensure work complies with applicable company policies, GxP requirements and relevant legal or regulatory obligations

Stakeholder Collaboration and Communication

• Build effective working relationships across Supply Chain, IT, Finance, Quality, Regulatory and other relevant functions.

• Present data stories through concise reports, dashboards, visualisations and presentations suited to the audience.

• Seek feedback, incorporate subject-matter expertise and communicate uncertainty without overstating conclusions.

• Share learning, reusable methods and good practice with the wider data and analytics community.

Apprenticeship and Professional Development

• Complete the BSc (Hons) Data Science programme, required learning activities and end-point assessment.

• Use protected off-the-job learning time in line with the apprenticeship programme and applicable funding rules.

• Maintain an evidence portfolio demonstrating development against the apprenticeship, knowledge, skills and behaviours.

• Attend progress reviews with the line manager and training provider, agree development priorities and apply learning to live business challenges.

Typical Work-Based Learning Assignments

• Build a data-quality profile for a priority supply chain dataset and recommend corrective actions.

• Develop a demand, inventory, service or process-performance dashboard with documented measures and data lineage.

• Create and evaluate forecasting, classification, anomaly-detection or optimization prototype for an agreed business problem.

• Automate a repeatable analytical task using approved code, controls and testing.

• Present findings and recommendations to a cross-functional stakeholder group and capture decisions and benefits.

Working Conditions

Typical hybrid office and remote-working environment, with infrequent business travel where required. The role is primarily desk based and requires sustained concentration, effective management of study and workplace commitments, and the ability to work with complex information across functional boundaries.

Measures of Success

Successful progression through academic modules, workplace reviews and required apprenticeship milestones. Accurate, documented and reproducible analytical work delivered to agreed quality and control standards. Useful insights or analytical products that address clearly defined supply chain needs. Positive stakeholder feedback on clarity, collaboration and practical application of learning. Demonstrated growth in the required apprenticeship knowledge, skills and behaviours.