Principal Data Scientist

Lahore, Punjab () 1 Positions

Job Description

Define the technical roadmap and architecture for the full fraud suite: transaction fraud detection, 3DS friction reduction, enrolment fraud, merchant fraud, cardholder risk scoring, CSR-quality NLP scoring, the internal RAG chatbot, and the agentic digital-CSR system.
Lead an organization of ~20, including senior data scientists - set standards, grow talent, align teams on priorities, and resolve both internal and external conflicts.
Own the most complex client escalations: when banks report missed fraud or false declines, lead the root-cause analysis, drive systemic pipeline improvements, and discover new features that raise precision and recall across the suite.
Champion feature and model innovation - set the bar for hypothesis-driven EDA, evaluate emerging techniques (deep learning, LLMs, RAG, agentic AI), and decide where to invest.
Engage executives and clients directly - represent the data science function to senior management and banking partners, present results and strategy, and address concerns with credibility.
Establish platform and MLOps standards for scale, reproducibility, monitoring, and reliability across all teams.
De-risk delivery: balance technical excellence with business outcomes, timelines, and regulatory/compliance expectations in a financial-services context.

Education: BS/MS - CS/Statistics/Mathematics/DS/Engineering
Experience: 10+ years of data science / ML experience with a strong record of production models at scale.

Deep expertise in Python and advanced SQL, plus strong command of ML/DL theory / MLand practice (imbalanced classification, calibration, interpretability).
Demonstrated success architecting end-to-end ML systems and setting technical strategy.
Proven ability to lead ~20 people, including senior Data Scientists, and to resolve internal and external conflicts.
Excellent executive presence - able to influence management and clients and translate technical work into business value.
Strong grasp of fraud/risk or payments problem domains.

Nice-to-have (bonus)

Production experience with LLMs, RAG, and Agentic AI systems (e.g., internal knowledge chatbots, digital CSR agents).
Hands-on depth in deep learning, XGBoost, and NLP.
Direct experience in issuer processing / card payments - authorizations, 3DS, enrolment, merchant risk, chargebacks.
Track record of published work and thought leadership in fraud, risk, or applied ML.
Experience with Data Engineering and MLOps tools (like Docker/Kubernetes, Spark, Airflow), Git, and visualization tools (matplotlib/plotly).