Senior Data Scientist

Deeplight; United Arab Emirates; On-site; AED 367,000 - 515,000; Full time

Job summary

A technology-driven AI company based in the UAE is seeking a Senior AI/Data Scientist to design and implement risk models for banking. The ideal candidate will have over 8 years of experience in data science, particularly within financial services, and deep expertise in statistical modeling. This role offers competitive salary, performance bonuses, and opportunities for professional growth in cutting-edge AI projects.

Benefits

Competitive salary and performance bonuses

Comprehensive health insurance

Professional development support

Opportunity to work on cutting-edge AI projects

Flexible working arrangements

Qualifications

• 8+ years of experience in data science and statistical modeling within banking/financial services.
• Proven track record developing risk models for corporate banking, wholesale banking, or treasury functions.
• Strong proficiency in Python and statistical concepts.

Responsibilities

• Develop risk models for treasury operations and corporate lending.
• Build optimization frameworks for improving risk‑adjusted returns.
• Create credit risk statistical models for lending decisions.

Skills

Statistical modeling

Risk modeling

Python

Attention to detail

Data analysis

Education

Advanced degree in Statistics, Mathematics, Data Science or related field

Tools

Statistical/ML frameworks

Job description

About DeepLight AI

DeepLight AI is a specialist AI partner with extensive experience implementing intelligent enterprise systems across multiple industries, with particular depth in financial services and banking.

Role Overview

We are looking for a Senior AI/Data Scientist with deep banking subject matter expertise to design, develop and implement statistical models and data‑driven solutions for our enterprise clients. The successful candidates will work on risk modeling, portfolio optimization, and regulatory capital management across treasury, wholesale banking, and corporate lending divisions.

Key Responsibilities

• Develop and implement risk models for treasury operations, corporate lending, wholesale banking, and liquidity management, with deep understanding of the underlying business context.
• Build RAROC and RAC optimization frameworks to improve risk‑adjusted returns and capital allocation across business lines.
• Create statistical models for corporate credit risk, including PD, LGD, and EAD models for lending decisions and portfolio management.
• Provide subject matter expertise on Basel requirements, regulatory capital models, and stress testing frameworks.
• Partner with business stakeholders in treasury, risk, and corporate banking to translate business requirements into statistical solutions.

Benefits & Growth Opportunities

• Competitive salary and performance bonuses
• Comprehensive health insurance
• Professional development and certification support
• Opportunity to work on cutting‑edge AI projects
• International exposure and travel opportunities
• Flexible working arrangements
• Career advancement opportunities in a rapidly growing AI company

This position offers a unique opportunity to shape the future of AI implementation while working with a talented team of professionals at the forefront of technological innovation. The successful candidate will play a crucial role in driving our company's success in delivering transformative AI solutions to our clients.

Requirements

• 8+ years of experience in data science and statistical modeling within banking/financial services.
• Proven track record developing risk models for corporate banking, wholesale banking, or treasury functions.
• Advanced degree in Statistics, Mathematics, Data Science, or related quantitative field preferred.
• Strong proficiency in Python and relevant statistical/ML frameworks but more importantly, deep understanding of the statistical concepts underlying these tools.
• Hands‑on experience with regulatory capital models and Basel framework.
• Strong problem‑solving skills and attention to detail.

Note: We're looking for true data scientists with solid statistical foundations AND proven banking domain expertise, not software engineers who've picked up some ML libraries. The ideal candidate has actually built and deployed risk models in production banking environments and can serve as a subject matter expert for our clients.