12 mei 2026
Food Technology Data Specialist - TEMP
| Branche | Zie onder |
| Dienstverband | Zie onder |
| Uren | Zie onder |
| Locatie | Utrecht |
| Opleidingsniveau | WO / master |
| Contactpersoon |
Olawale Olukunga +31622258890 |
Informatie
Technical Data & Sample Management:
- Collect, organize, and ensure traceability of all technical data (losses, analytics, product performance) to support product development and Right by Design initiatives. Coordinate sample collection, shipment, and analysis, and follow up on any issues that arise;
Root Cause & Project Support:
- Gather data for investigations to help identify root causes of deviations. Support large-scale analysis projects by tracking samples, performing data checks, and structuring outputs for modeling;
- Support the preparation of clear technical summaries for project leads and subject matter experts.
- Coordinate additional sampling or analysis where required;
Recipe Predictability:
- Contribute to improving process and shelf-life loss assumptions by identifying discrepancies between expected and observed results, strengthening data for future formulation decisions;
- Perform first-level data quality checks before results are used for further technical or statistical interpretation;
- Help identify gaps between expected recipe calculations and observed analytical results;
- Help convert analytical outputs into structured datasets that can support future process loss, shelf-life loss, and recipe predictability models.
Omschrijving
Technical Data & Sample Management:
- Collect, organize, and ensure traceability of all technical data (losses, analytics, product performance) to support product development and Right by Design initiatives. Coordinate sample collection, shipment, and analysis, and follow up on any issues that arise;
Root Cause & Project Support:
- Gather data for investigations to help identify root causes of deviations. Support large-scale analysis projects by tracking samples, performing data checks, and structuring outputs for modeling;
- Support the preparation of clear technical summaries for project leads and subject matter experts.
- Coordinate additional sampling or analysis where required;
Recipe Predictability:
- Contribute to improving process and shelf-life loss assumptions by identifying discrepancies between expected and observed results, strengthening data for future formulation decisions;
- Perform first-level data quality checks before results are used for further technical or statistical interpretation;
- Help identify gaps between expected recipe calculations and observed analytical results;
- Help convert analytical outputs into structured datasets that can support future process loss, shelf-life loss, and recipe predictability models.
Functie eisen
- Bachelor’s degree in food technology, analytical chemistry or equivalent practical experience in one of the following areas;
- Food or nutrition product development, Analytical testing of food products, ingredients, or nutritional compositions, Sample collection, sample preparation, and sample shipment;
- Shelf-life studies, process trials, production samples, or stability testing;
- Structured data collection, data tracking, and technical documentation;
- Working in a regulated food, medical nutrition, infant nutrition, or FSMP environment is a strong advantage;
- Experience in a temporary project, consultancy, or external assignment environment is preferred;
- Nutrient analysis and analytical variability;
- Basic food technology principles, preferably in liquid and/or powder products;
- Recipe calculation logic, including ingredient contribution, overages, process losses, shelf-life losses, and label targets;
- Excel or similar tools for data tracking, calculations, and dataset preparation;
- Structured and accurate way of working;
- Strong attention to detail;
- Ability to manage multiple samples, products, results, timelines, and stakeholders in parallel;
- Practical problem-solving mindset;
- Good communication skills with laboratories, factories, product developers, and project leads;
- Comfortable working with incomplete or imperfect data;
- Able to distinguish between operational sample issues and technically meaningful deviations;
- Proactive in following up on missing data, shipment delays, unclear analytical results, or documentation gaps.
