Manufacturing Statistician: Data-Driven Process Insight

Abbott; Donegal; On-site; EUR 60,000 - 90,000; Full time

Job summary

Abbott in Ireland conducts data-driven analysis to support manufacturing processes and yield optimization across multiple sites, including Donegal. You will mine large datasets, interpret results, and provide analytical expertise to engineers and researchers.

Required are 4+ years in a similar role, a quantitative degree, and strong skills in Python, R, SAS or JMP. Experience with relational databases is preferred, enabling effective data-driven decision making.

Qualifications

• Bachelor's degree or higher in a quantitative field (e.g. Mathematics, Data Science, Statistics).
• Minimum of 4 years experience in a similar role.
• Proficient in a programming language such as Python, R, SAS, JMP or similar.
• Understanding of probability, statistical process control, hypothesis testing, time series analysis, regression.

Responsibilities

• Data mine large, complex datasets to answer research questions.
• Provide real-time data analysis support to manufacturing.
• Interpret statistical findings into business context for stakeholders.
• Consult on data analysis and statistical tools to junior team members and engineers.
• Assist with introduction of analytical test methods and development trials.
• Collaborate with scientists and engineers to design experiments.

Skills

Data mining

Statistical analysis

Data visualization

Real-time analytics

Process optimization

Education

Bachelor's degree in a quantitative field

Tools

Python

R

SAS

JMP

Oracle Databases

MySQL

Microsoft Access

Job description

Abbott in Ireland conducts data-driven analysis to support manufacturing processes and yield optimization across multiple sites, including Donegal. You will mine large datasets, interpret results, and provide analytical expertise to engineers and researchers.

Required are 4+ years in a similar role, a quantitative degree, and strong skills in Python, R, SAS or JMP. Experience with relational databases is preferred, enabling effective data-driven decision making.