Data Science Specialist

• Lead end to end data science and statistical projects to address manufacturing challenges, applying advanced methods (DOE, SPC, regression, GLMs, multivariate analysis, optimization, simulation, predictive modeling, machine learning, etc.).
• Perform data quality assessments, maintain metadata awareness, and prepare reports for stakeholders and regulatory submissions.
• Develop reproducible, well documented code and data science pipelines.
• Automate repeatable tasks and integrate data science into operational workflows.
• Communicate complex statistical and modeling results clearly, to both technical and non-technical audiences, and provide actionable recommendations.
• Stay current with modern predictive modeling, statistical methods, and machine learning methodologies.
• Collaborate closely with Operations, Quality, Engineering, and Regulatory teams across regions.

• MSc (or higher) in Statistics, Mathematics, Computer Science, Physics, Economics, or related field with strong statistical and data science emphasis.
• Minimum 2 years’ hands-on experience in a data science, statistician or biostatistics role, preferably in pharmaceutical or manufacturing environments.

• Knowledge of DOE, SPC, GLMs, multivariate methods, simulations, predictive modeling, time series, stochastic processes, optimization, and machine learning.
• Proficiency in R or Python.
• Strong coding practices, with experience using collaborative tools (e.g., Git).
• Ability to work with incomplete or imperfect data and interpret results within the manufacturing/business context.
• Strong analytical and problem-solving skills.
• Excellent communication and interpersonal skills.
• Comfortable working independently as well as collaboratively in a global team.

• Experience applying AI methodologies.
• Familiarity with distributed compute environments (e.g., Spark).
• Experience generating statistical reports for regulatory filings or audits.
• Exposure to Agile/DevOps practices and tools (JIRA, Confluence, Git).
• Willingness to travel.

• Salary above industry standard, reflecting the level of expertise and impact of the role
• Hybrid working model, with flexibility to work remotely and a minimum of 1 day per week onsite
• Opportunity to work in a regulated, global pharmaceutical environment, applying advanced statistics and data science to real manufacturing challenges
• Long-term cooperation, including the possibility of transitioning to an internal position within the organization
• Exposure to international projects and cross-functional collaboration across Operations, Quality, Engineering, and Regulatory teams
• Access to a broad range of employee benefits, supporting both personal development and work-life balance
• A stable and structured working environment with room for career growth and continuous learning
• Clear project ownership and the opportunity to see tangible business impact of your work

Axial