Data Analyst / Senior Data Analyst — Product Analytics & Applied Data Science

About PILLAR:

PILLAR (formerly Ringkas) builds AI-native infrastructure for financial institutions. We started in 2022 as Southeast Asia's embedded mortgage platform and now run the AI layer banks use to originate, qualify, and convert lending customers.

The role:

Our AI engineering team runs the agent. You're the analytical brain behind it — sharpening the frameworks for each customer segment, extracting insight from growing volumes of conversation data, and improving how the agent reads and responds to each type of customer.

You'll also own product and business analytics with the Head of Product: instrumentation, experiments, dashboards, and the analysis that decides what we build next.

What you'll do:

• Sit in product reviews before engineering starts, and turn product goals into metric specs — what's trackable, what isn't, flagged before code is written.
• Keep behavioral analytics honest: validate events, find the gaps, write the spec that fixes them.
• Write complex SQL against raw tables, and build dashboards the team uses without asking you.
• Run experiments end to end — size them, read them, and say clearly when a result is inconclusive.
• Turn patterns in retention, drop-off, and adoption into a recommendation someone can act on.
• Build scoring models that feed the product directly: lead propensity, drop-off prediction, match quality.
• Design evaluation for our AI Agent — offline eval sets, LLM-as-judge calibration, and the call on whether a regression is real or noise.

What we require:

We hire for reasoning ability, not resume length. Recent graduates and people with five years of experience are evaluated the same way.

• A genuine statistics foundation. What a p-value actually means, why a result can be significant and useless, how you'd size a test.
• Structuring ability. Given an ambiguous question, you decompose it, decide what's worth measuring, and defend your approach. This is what we screen hardest for.
• Clear communication. You'll write for banks and executives — an analysis nobody understands is worth nothing.
• Real SQL. Window functions, debugging a join that's silently duplicating rows, working against raw tables. We test this.
• Python — pandas for cohort work, and enough statistics libraries to run a test properly.
• Daily use of AI tools in real work.
• English and Bahasa Indonesia. Arabic is a plus — our conversation data is multilingual.

Education Requirements:

Degree in Statistics, Mathematics, Actuarial Science, Data Science, Computer Science/Informatics, Industrial Engineering, Information Systems, Economics/Econometrics, Operations Research, or other quantitative fields. Graduates from universities included in the QS World University Rankings 2027 Top 1000 are highly preferred. We also welcome candidates with equivalent academic achievements or strong relevant experience, and value strong academic performance, analytical research/thesis, competitions such as Kaggle or datathons, scholarships, and relevant projects or publications.