Senior Data Scientist — Transaction Intelligence

What you'll work on

• Own and level up transaction enrichment — the foundation everything else stands on: categorization that generalizes to merchants we've never seen, and merchant name matching that tolerates harmless variation without confusing genuinely different businesses.
• Own model confidence across our systems: well-calibrated probabilities, principled abstention on uncertain cases, and confidence-based routing that products and review workflows rely on.
• Build transaction-based insight models for other services: pattern recognition, anomaly detection, and behavioral analysis on transaction streams.
• Own the ongoing validation of our risk models: backtesting scores against realized users behavior, monitoring discrimination and calibration as behaviors evolve, and driving model improvements from what the data shows.
• Handle our text and behavior data as they really are: bilingual, informally romanized Arabic with unstable spelling; transaction streams with truncation artifacts, bank quirks, and heavy- tailed distributions.
• Build efficient batch pipelines at hundreds-of-millions-record scale, designed to be re-run routinely as models improve.
• Design within the guardrails of a regulated fintech: data governance, privacy, and cybersecurity requirements shape what data we can use, where workloads can run, and which tools we can adopt — you'll build excellent solutions inside those boundaries, working with those teams rather than around them.
• Build the evaluation discipline these systems deserve: labeled datasets, regression test suites for model behavior, and metrics that answer "did this change help?" for every release.

Must-have qualifications

• 3+ years of applied ML/data science, with models shipped and maintained in production at scale — and knowledge of their failure modes.
• Strong analytical range beyond modeling: exploratory analysis, statistical rigor, and feature design on behavioral/tabular data (SQL fluency assumed).
• Hands-on experience with text similarity, fuzzy matching, or entity resolution on noisy real- world strings.
• Strong Python and the scientific stack (scikit-learn, scipy, numpy), with performance and cost awareness at scale — vectorization, sparse data structures, efficient batch computation.
• Demonstrated maturity working under externally imposed constraints — data governance, privacy, cybersecurity, compliance, infrastructure policy — including collaborating with the teams who own them.
• A track record of turning ambiguous "the model feels wrong" complaints into measured, regression-tested properties.
• Comfort reading and debugging model code you didn't write, and working directly with product teams on loosely-defined problems.

• Arabic / Arabizi text processing — a strong plus; our data is bilingual with unstable romanization.
• Credit or behavioral risk modeling and validation — scorecards, discrimination and calibration measurement, backtesting against realized outcomes.
• Text embeddings and approximate nearest-neighbor retrieval in resource-conscious settings.
• LLM-assisted labeling, distillation, or weak-supervision pipelines.
• ML lifecycle and batch-serving tooling (experiment tracking, data versioning, distributed task queues).