The scoreboard, and what is not on it. The hard targets, how often each is actually
looked at, and the quiet signals that never reach a dashboard.
Experimental Data Quality
The accuracy and completeness of the data you record in the Electronic Lab Notebook (ELN) and associated systems.
Target · <2% error rate on all recorded experimental data If you record 100 data points in a week, we'd expect no more than 2 minor errors (e.g., incorrect unit, missing timestamp) and zero critical errors (e.g., mislabelled sample, incorrect measurement value).
Experimental Throughput
The number of distinct experimental runs or analyses you complete within a given timeframe, reflecting your efficiency in the lab.
Target · Completes an average of 15 experimental runs per week (this varies by project complexity, of course, but it's a good rough guide) In a typical week, you might run 5 batches of synthesis, perform 5 analytical characterisations, and set up 5 long-term stability studies. This is about getting things done, not just starting them.
Compliance & Documentation Adherence
Ensuring all your work, from experimental design to data recording, follows our internal Standard Operating Procedures (SOPs) and regulatory guidelines.
Target · 100% on-time completion of ELN entries, training modules, and adherence to all relevant SOPs You'll consistently complete your ELN entries within 24 hours of experiment completion, pass all mandatory safety training on time, and ensure every reagent is correctly logged in our inventory system.
Troubleshooting & Problem Resolution Rate
Your ability to identify and resolve common experimental or instrument issues without needing constant escalation.
Target · Resolves 80% of routine technical issues independently, escalating only novel or complex problems When the GC-MS gives unexpected peaks, you'll systematically check the column, gas lines, and calibration before asking for help. If it's a software bug, that's when you'd escalate.
Scientific Rigour & Critical Thinking
Your approach to experimental design, data interpretation, and questioning assumptions, even your own. We want scientists, not just technicians.
- You'll consistently include appropriate controls in your experiments, actively challenge ambiguous data, and proactively suggest alternative hypotheses. During discussions, you'll ask 'how could this be wrong?' or 'what else could explain this result?' before jumping to conclusions. Your experimental reports won't just present data
- they'll offer thoughtful interpretations and next steps.
Proactive Problem Solving
Your initiative in identifying potential issues before they become major problems and proposing well-thought-out solutions.
- You'll flag potential equipment bottlenecks before they impact your timeline, suggest improvements to existing experimental protocols, or spot inconsistencies in data trends that others might miss. You won't just bring problems
- you'll bring potential solutions, even if they're not perfect.
Effective Collaboration & Knowledge Sharing
How well you work with your immediate team and cross-functional colleagues, sharing your findings and learning from others.
- You'll actively participate in team meetings, offering constructive feedback on others' work and clearly explaining your own. You'll proactively reach out to colleagues in Product Development or Process Engineering to understand their needs. You'll also contribute to our shared knowledge base, perhaps by writing a clear SOP for a new technique you've mastered.
Adaptability to Changing Priorities
Your ability to adjust your plans and focus when project priorities inevitably shift or unexpected results force a change of direction.
- When a project gets deprioritised or a new 'urgent' request comes in, you'll quickly re-organise your lab schedule without significant friction. You'll show a willingness to pivot your experimental focus based on new data or business needs, rather than rigidly sticking to your original plan.
