Dataset Lead, Scientific Research

Reinforced Learning; Town of Tonawanda (NY); Vor Ort; USD 95.000 - 150.000; Vollzeit

Zusammenfassung

Reinforced Learning in the United States seeks a research-focused scientist to lead experimental design, data interpretation and literature synthesis. You’ll manage programs across biology, chemistry and physics, collaborating with PhD- and postdoc-level researchers to create tasks that test scientific reasoning.

Your PhD in a natural science with a track record of reviewing others’ work, strong writing, and the ability to run several task families at once are essential.

Qualifikationen

• A PhD in a natural science with post-PhD research experience.
• Breadth across methods and a track record of reviewing other people’s work.
• Strong writing and the organisation to run several task families at once.
• Python or R for handling data.

Aufgaben

• Task families in biology, chemistry, physics and adjacent fields, defined with partner research teams.
• A contributor bench of researchers at the PhD and postdoctoral level.
• Rubrics that distinguish a plausible explanation from a correct one.
• Fixtures built from real or realistic experimental data.
• Review of delivered work for accuracy and reproducibility.

Kenntnisse

Breadth across methods

Strong writing

Reviewing others' work

Python or R

Ausbildung

PhD in natural science

Jobbeschreibung

The role Experimental design, data interpretation, literature synthesis, protocol troubleshooting. You’ll lead programs in the natural sciences where researchers create tasks that demand scientific reasoning rather than recall.

What you’ll own

• Task families in biology, chemistry, physics and adjacent fields, defined with partner research teams.
• A contributor bench of researchers at the PhD and postdoctoral level.
• Rubrics that distinguish a plausible explanation from a correct one.
• Fixtures built from real or realistic experimental data.
• Review of delivered work for accuracy and reproducibility.

What you bring

• A PhD in a natural science and research experience beyond it.
• Breadth across methods and a track record of reviewing other people’s work.
• Strong writing and the organisation to run several task families at once.
• Python or R for handling data.

Helpful, not required

• Peer review, grant review or graduate-level teaching.
• Familiarity with how models are evaluated in scientific domains.