Senior Data Scientist
Payable Pvt LtdThe Role
This is a hands-on role for someone who wants to own a risk model from first advance to first retrain. We advance funds against a merchant’s daily card settlements and collect through a variety of payment methods. We decide who qualifies, and for how much, from transaction history and other data sources. Today that runs on transparent, documented rules. You run them as the book grows, turn every advance into training data, train the first real models once there are enough outcomes — and prove they beat the rules. We are building for the AI-led era — a team that runs at high capacity rather than one that scales by headcount. Today that is the CEO, a Head of Operations and an operations team, a Technical Product Manager (hiring now), a contract engineering team, and a data science advisor who built and documented the scoring approach. You take it on from day one — you are the data function.
Questions and answers
Who is hiring for Senior Data Scientist?
Payable Pvt Ltd advertised this Full-time vacancy on Agency.lk. You can submit an application on this page.
What does this job involve?
The decision — the scoring and offer-sizing rules behind every advance: who, how much, and why. Transparent, versioned, defended on the numbers; The path from rules to models — labels, decision-time snapshots, the first trained scores; shadow, champion-challenger, promotion, with the rules as fallback; How the book is doing — collection pace, early…
What qualifications and skills are required?
3–5+ years of applied machine learning in production with real decisions riding on it — you’ve owned a model end to end and know when a percentile rank beats a deep net; Fluent in Python (pandas, scikit-learn, gradient boosting) and SQL; you build with AI tooling — Claude Code, Cursor and the like — and ship real work with it; You turn raw logs into clean…
Where is this job located?
The advertised location is Colombo, Sri Lanka. The work mode is On-site. Check the advert for travel and eligibility requirements.
Is remote work available?
The employer lists the work mode as On-site. Confirm any flexibility with the employer using the information in the advert.
When was this job posted?
This vacancy was posted on 3 Nov 2026.
How do I apply for this job?
Read the full advert, then use the application form on this page to provide your contact details and CV. Sign in if you already have an account.
When does the application period end?
No separate closing date is shown for this vacancy. Check the job description for any application dates and confirm that the listing is still accepting applications.
Responsibilities
- The decision — the scoring and offer-sizing rules behind every advance: who, how much, and why. Transparent, versioned, defended on the numbers
- The path from rules to models — labels, decision-time snapshots, the first trained scores; shadow, champion-challenger, promotion, with the rules as fallback
- How the book is doing — collection pace, early warnings, shortfall and recovery by cohort, so the team acts on numbers
- The merchant data — bank and network extracts reconciled, de-duplicated and versioned; you know the gaps and chase the data owners until they close
- - The numbers the business runs on — eligible pools, cohort sizing, expected loss, seasonality — ready when the CEO and ops need them
- The label and feature pipeline — merchant-day series, decision-time snapshots and advance outcomes, versioned by data vintage, so the first model trains leakage-free the week labels exist
- The first trained scores — a behaviour score for collections, an application score for new merchants, and the harness that decides whether they replace the rules
- - The reporting the team runs the book on — collections views, cohort read-outs, outcome tables by band. Scripts and dashboards, not slide decks
Requirements
- 3–5+ years of applied machine learning in production with real decisions riding on it — you’ve owned a model end to end and know when a percentile rank beats a deep net
- Fluent in Python (pandas, scikit-learn, gradient boosting) and SQL; you build with AI tooling — Claude Code, Cursor and the like — and ship real work with it
- You turn raw logs into clean features, defend every number, and design pilots that yield next year’s labels
- Risk scoring, payments or SME working-capital experience is a strong plus — scorecards, PD models, collections, reject inference — or show us you learn new domains fast
- - A doer. You chase the missing extract, close the loop with the data owner, and take pride in the unglamorous work that makes a model trustworthy.
Benefits
About Company
Fintech & Payments
Since its inception in 2016, PAYable has empowered over 22,000 MSME merchants across Sri Lanka, effectively making it the single largest mobile POS service provider in the country. By eliminating all major cost barriers, PAYable has successfully motivated merchants - who were thus far excluded from the ability to accept cashless payments - to adopt and champion credit and debit card payment acceptance. PAYable currently powers transactions on the VISA, Mastercard, CUP, AMEX, JCB, Lanka Pay and Alipay networks, is completely PA-DSS compliant and offers end-to-end security, ensuring cardholder information remains 100% secure. Built to foster inclusivity, PAYable technology is able to adapt to business integration requirements, map customers business processes, integrate to 3rd party in-app and hardware solutions and conform to international standards.
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Data & Analytics > Machine Learning > Data Science
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