Senior Domain Expert

Syndio•Home, WV

21h•$143,000 - $160,000•Remote

About The Position

Syndio is the leader in compensation equity and pay decision intelligence, helping the world's largest employers make fair, defensible, and transparent pay decisions at scale. As Decisions moves to general availability and our enterprise footprint grows, the quantitative depth behind our recommendations has to keep pace. The Senior Domain Expert (Labor Economics, Statistics, or IO) is a hands-on quantitative practitioner on the Applied Pay Intelligence team. You work directly with our customers and are a senior individual contributor role that leads a small squad. You are the person our customers, our product, and the rest of the team turn to when the methodology has to hold up under scrutiny. You own an area of Syndio's methodology and asset library, build the 1:many resources that scale your expertise across the customer base, partner directly with Product to embed methodology into Decisions and Essentials, and show up in the customer conversations where the numbers and the reasoning have to be right. You will translate custom analytics into product requirements and support product and development teams as needed on design and implementation plans, partner with the data science and development teams to ensure the product helps advance the outcomes of our total rewards customers.

Requirements

5+ years of hands-on quantitative work in labor economics, statistics, econometrics, industrial-organizational psychology, or a directly related field, applied to compensation, workforce, or pay equity questions.
Deep facility with regression modeling and comparator methodology. You can build a defensible pay equity model, explain what it does and does not tell you, and defend the choices behind it in front of a non-technical audience.
Demonstrated ability to translate quantitative rigor into language a CHRO, a works council, or a CSM can act on, without losing the substance.
Experience embedding methodology into a product, a tool, or a repeatable workflow that other people execute — not just running the analysis yourself.
Strong judgment on the difference between what the data supports and what the customer wants it to say, and comfort holding that line.
Track record of building content, documentation, or enablement materials that reduce reliance on your calendar over time.
Comfort thinking about how quantitative expertise translates into AI-mediated product capability.

Nice To Haves

Experience in HR tech, compensation, people analytics, or a similarly data-intensive enterprise domain is a strong plus.

Responsibilities

Lead a squad. You will be an individual contributor but you will own an area of Syndio's methodology and content — comparator group logic, regression modeling, remediation methodology, or a related domain. Guide the squad's roadmap, prioritize what gets built, and hold the technical bar for the work that ships.
Apply methodology in complex customer situations. Work through the statistical questions customers can't resolve inside the product: unusual workforce structures, small-group modeling, contested comparator choices, novel regulatory tests. Bring the methods, explain the tradeoffs, defend the approach.
Build 1:many resources. Turn recurring methodological questions into durable content — customer briefings, methodology guides, model documentation, worked examples — so recurring questions do not require a recurring meeting.
Partner with Product. Work directly with Product and AI teams to embed methodology into Decisions and Essentials. Translate quantitative rigor into product logic, review model outputs, catch the edge cases before customers do.
Show up for the moments that matter. Anchor customer QBRs, regulatory inflection points, and board-adjacent conversations where the methodology has to hold. Not every meeting — the ones where getting the math and the framing right changes the outcome.
Feed the frameworks upward. Surface what current frameworks don't yet cover. Bring specific customer patterns and methodological gaps to the Principal-level colleagues and to Product so the platform's intelligence keeps compounding.