Head Of Data Science & Credit Risk

Jobgether SRL; Saudi Arabia; On-site; SAR 600,000 - 1,000,000; Full time

Benefits offered by this job

Competitive compensation

Equity program

Leadership opportunity

Growth opportunities

Modern tech stack

International scope

Meaningful impact

Mission-driven environment

Job summary

Jobgether SRL, a fintech innovator, seeks a Head of Data Science & Credit Risk to steer ML-powered underwriting across diverse markets. You will own the full model lifecycle from development to monitoring and translate analytics into practical decisions that drive growth.

You will lead risk policies, portfolio health, and responsible lending, while mentoring a high-performing team of data scientists and risk analysts in a fast-paced environment.

Qualifications

• 10+ years of combined data science, ML, and consumer credit risk experience.
• Experience building and scaling credit risk policies and portfolios across multiple products/markets.
• Production ML in real-time or near-real-time decisioning environments.
• Strong hands-on experimentation and A/B testing experience.
• Solid mathematical and statistical foundations blending classical stats with ML.
• Proficiency in SQL and data exploration; cloud platform experience.
• Familiarity with cloud data infrastructure; BigQuery is a plus.
• Experience with MLOps tools (e.g., MLflow) is a plus.
• Regulatory knowledge including IFRS 9 is beneficial.

Responsibilities

• Lead ML model development for credit decisioning, fraud detection, risk segmentation, and monetization.
• Develop underwriting algorithms using alternative data to expand access responsibly.
• Build real-time scoring models across multiple markets and products.
• Ensure model governance: interpretability, robustness, and drift monitoring.
• Establish MLOps practices: experimentation, versioning, deployment, monitoring.
• Develop and manage credit risk frameworks, policies, and customer segmentation.
• Monitor portfolio metrics, investigate changes, and establish early-warning indicators.
• Lead A/B testing and policy simulations with KPI-driven decisioning improvements.
• Collaborate with Finance on provisioning and capital allocation; stress testing support.
• Mentor data scientists and risk analysts; guide technical strategy and execution.
• Present model performance and strategic recommendations to executives.

Skills

Strategic leadership

Data science leadership

Communication

ML & risk models

Experimentation

Cloud platforms

Tools

SQL

BigQuery (GCP)

MLflow

Job description

As Head of Data Science & Credit Risk, you will lead the strategy behind ML-powered underwriting and credit risk decisioning across multiple fast-growing markets.

You will own the full lifecycle of risk models, from development and deployment through monitoring, experimentation, and measurable business impact.

The role combines deep technical leadership with responsibility for credit policies, portfolio performance, and responsible lending practices.

You will build and mentor a high-performing team of data scientists and risk analysts while remaining actively involved in complex technical challenges.

Working closely with Engineering, Product, Finance, and executive stakeholders, you will turn advanced analytics into practical decisions that support sustainable growth.

You will help improve approval rates, automate decisioning, identify new customer segments, and strengthen portfolio health through data-driven strategies.

This is a greenfield leadership opportunity in a mission-driven fintech environment focused on expanding fairer and more accessible financial services.

Accountabilities

• ML and model development: Lead the design, testing, deployment, and ongoing improvement of machine learning models for credit decisioning, fraud detection, risk segmentation, customer value, monetization, and marketing attribution.
• Underwriting innovation: Develop underwriting algorithms using alternative data sources to strengthen risk assessment while responsibly expanding access to financial services.
• Real-time decisioning: Build and scale real-time or near-real-time scoring models across multiple markets and products.
• Model governance: Ensure models are interpretable, robust, fair, and reliable, with appropriate monitoring for accuracy, feature stability, performance, and drift.
• MLOps: Establish strong practices for experimentation, model versioning, deployment, monitoring, and production lifecycle management.
• Credit risk strategy: Develop and manage credit risk frameworks, policies, approval strategies, risk thresholds, and customer segmentation approaches adapted to individual markets.
• Portfolio monitoring: Track portfolio and risk metrics, investigate material changes, and establish early-warning indicators for potential deterioration.
• Experimentation: Simulate policy and model changes, lead A/B testing, and use performance data and business KPIs to continuously refine decisioning strategies.
• Stress testing and provisioning: Lead stress testing and expected credit loss modeling while partnering with Finance on provisioning and capital allocation.
• Market expansion: Develop localized risk models and policies that support expansion into new markets while aligning with applicable regulatory requirements.
• Team leadership: Build, lead, coach, and mentor data scientists and risk analysts while remaining hands-on with technical problem-solving and model development.
• Strategic planning: Own the data science and credit risk roadmap, aligning priorities with business growth, product development, and market expansion objectives.
• Executive communication: Present model performance, portfolio trends, analytical insights, and strategic recommendations clearly to executive leadership and board-level stakeholders.
• Cross-functional partnership: Work closely with Engineering, Product, and Finance to translate analytical findings into measurable business outcomes.
• External partnerships: Evaluate and establish relationships with alternative data providers and credit bureaus.
• Business impact: Improve approval rates while maintaining target default rates and responsible lending standards, reduce time-to-decision, strengthen unit economics, and identify new customer and product opportunities.

Requirements

• Professional experience: 10+ years of combined experience across data science, machine learning, and consumer credit risk, ideally within fintech, digital lending, BNPL, or earned wage access.
• Credit risk leadership: Proven experience developing and managing credit policies and portfolios at scale across multiple products, markets, or both.
• Production ML: Demonstrated success building, deploying, and monitoring production machine learning models within real-time or near-real-time decisioning environments.
• Experimentation: Strong hands-on experience with experimentation and A/B testing to assess the impact of model and policy changes.
• Statistical expertise: Strong mathematical and statistical foundations, combining classical statistical techniques with modern machine learning approaches.
• Data skills: Strong proficiency in SQL and exploratory data analysis, alongside practical experience working with cloud-based data platforms.
• Cloud technology: Experience with cloud data infrastructure is required; familiarity with GCP BigQuery is useful but experience with this specific platform is not mandatory.
• Technical leadership: Experience building and leading technical teams while remaining actively engaged in model development, analytical work, and complex problem-solving.
• Communication: Excellent communication skills, with the ability to explain sophisticated models, risk concepts, and analytical recommendations to non-technical stakeholders.
• Business judgment: Strong commercial understanding and the ability to connect technical and risk decisions with growth, portfolio performance, unit economics, and return on investment.
• Startup mindset: Adaptable, proactive, and comfortable operating in a fast-paced environment where priorities can evolve quickly.
• Regional expertise: Familiarity with Southeast Asian credit markets, credit bureaus, and alternative data sources is an advantage.
• MLOps tools: Experience with MLflow or similar frameworks for production machine learning development and deployment is a plus.
• Regulatory knowledge: Understanding of IFRS 9 and local credit regulations across multiple markets or regions is beneficial.

Benefits

• Competitive compensation: Salary based on experience and location.
• Equity participation: Opportunity to participate in the company's equity program.
• Leadership opportunity: Build and shape a growing data science and credit risk function from the ground up.
• Professional growth: Opportunities to expand your leadership, technical expertise, and strategic influence within a rapidly growing organization.
• Modern technology: Work with a modern machine learning stack and cloud-based data infrastructure.
• International scope: Lead data science and credit risk initiatives across multiple markets and support international expansion.
• Meaningful impact: Help develop models and financial services designed to expand responsible access to financial products for underbanked employees.
• Mission-driven environment: Contribute to improving financial well-being through fairer, more accessible financial services.