About the role
We are looking for an Analytics Lead to join our Data team in Tel Aviv, and to be the senior voice on product and business analytics of the company.
You own the systems and narratives that turn our data into commercial outcomes: less churn, more expansion, faster sales, and smarter testing across the merchant portfolio. You join a small, full-stack data team and work across the analyst domains it touches (CS, CRO, Sales, SaaS metrics, finance, marketing). This is a hands-on lead role: you ship the work yourself and set the analytical quality bar others work to. You are self-driving and curious, and you act as a force multiplier who roughly doubles what the rest of the company can do with data and insight. It is a senior individual-contributor role: no direct reports, real influence.
What you'll own
- SaaS metrics, end to end. Own MRR, NRR, GRR, churn dollars, CAC, LTV, and payback, the monthly business close to Finance, and the metric definitions behind them.
- Product analytics. Own how PDQ measures its own product (feature adoption, activation, impact) and the definitions behind it, moving it from reporting to decisions.
- A/B learning that compound. Build the repository that mines patterns across wins and losses portfolio-wide, and feed the best ones back into the product and our AI agent.
- A CRO framework for 200+ merchants. Design the system that scales (segmentation, opportunity scoring, recommended strategies, expected impact, measurement) and let our AI agent and CSMs run per-merchant execution on it.
- Churn prevention, not churn reporting. Build the closed loop from churn signal to intervention to outcome, partnered with our data science team. Define what "saved" means and measure it.
- Post-activation ROI and expansion. Quantify the value each merchant gets after go-live and turn it into a live per-merchant scorecard Sales and CS use in renewals and upsells.
- Win/loss. Stand up the program from scratch: intake with Sales, a taxonomy, and a quarterly readout to leadership on why we win, why we lose, and what to change.
- The Sales and CS partnership. Be the data team's primary partner to both, with a real feedback loop from the field into analysis.
Requirements
- 6+ years in applied analytics in SaaS, a product company, or e-commerce, with DTC, e-commerce, or B2B2C experience strongly preferred.
- Full-stack analyst range: BI, data modeling, product analytics, experimentation and A/B testing, applied statistics, and churn analysis, with real depth in more than one of CS, CRO, Sales, SaaS metrics, finance, and marketing analytics.
- A/B testing experience. This one is required: you have designed and analyzed experiments and can separate a real effect from noise.
- Strong SQL and comfort in Python. Much of the technical part of analysis today is AI-generated. That reliance creates room for lower standards; we expect the opposite, so you read it closely and challenge it rather than trust it.
- Stakeholder fluency: comfortable with a CFO, a Sales VP, and a CSM in the same hour, fluent in the KPIs each lives by, and able to communicate in business language rather than p-values.
- A closed-loop instinct: not "report a metric" but "what changes because of this," with accountability for every number you ship.
- A clear view of where AI helps analytics and where it creates risk, and the rigor to reject sloppy output.
- High integrity, strong attention to detail, but equally comfortable rolling up your sleeves and doing hands‑on work.
- Ability to work in our Tel Aviv office, four days a week
- Full professional proficiency in English (written and spoken)
Strong advantages: Snowflake experience, and a real passion for online shopping, e-commerce, and consumer psychology, with genuine curiosity about how DTC shoppers behave. Also a plus: churn modeling, LTV/pLTV, win/loss frameworks, or B2B SaaS expansion analytics.