Staff Data Scientist - Experimentation & Causal Inference at Jobgether
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Jul 20, 2026
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Jul 22, 2026
What this job asks for AI summary
A senior individual contributor role centered on building and owning an experimentation and causal inference practice across a scaling technology company. The work involves defining statistical methodologies, experiment design standards, and causal inference frameworks, while partnering with product, engineering, analytics, and AI teams. Suited to someone with 9+ years in applied statistics or product data science who can operate without direct authority and communicate complex findings to executive audiences.
Staff level · 9+ years · Remote · Full-time
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $122,874 (middle half $87,544–$162,374).
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (8)
This role is posted via Jobgether on behalf of an unnamed partner company; the actual hiring employer and its specific product context are not disclosed.
The role is explicitly described as a hands-on individual contributor (not a people manager), consistent with the Staff band — it carries org-wide technical influence and executive visibility but no direct reports.
Python and R are listed as alternatives ('Python or R'); Python is named first and is the more common primary language in this space.
Causal inference techniques (difference-in-differences, instrumental variables, synthetic controls, matching methods) are enumerated in the requirements body with firm language and are marked as required individually.
Statsig is explicitly called out as 'a plus' and is marked preferred accordingly.
Experience building experimentation frameworks from scratch and background in B2B SaaS/CRM/PLG environments are listed as 'preferred' — no concrete named technologies were attached to these preferences beyond Statsig, so no additional skill entries were created.
No compensation figures were provided beyond a general reference to a 'competitive compensation package.'
Ignored 1 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): power analysis.
Read the full posting
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