Head Of Data Science & Credit Risk

Jobgether; Portugal; Presencial; EUR 120 000 - 180 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Equity participation

Competitive salary package

Modern ML stack

Autonomy in a fast-paced environment

Resumo da oferta

Jobgether in Portugal seeks a Head of Data Science & Credit Risk to define and scale core decisioning systems for fintech lending. You will lead ML-driven underwriting, portfolio risk analytics, and risk policy, while partnering with senior leadership to drive strategy and growth.

You will build a high-impact team of data scientists and risk analysts, advancing credit risk capabilities across markets in a fast-paced, data-driven environment.

Qualificações

• 10+ years in data science, ML, and consumer credit risk in fintech or lending.
• Experience deploying ML models in real-time decisioning systems.
• Proven leadership building technical teams and communicating with executives.
• Strong SQL and cloud data platform experience (GCP/BigQuery or equivalent).
• Familiarity with MLOps tooling and experimentation frameworks is a plus.

Responsabilidades

• Define and lead end-to-end data science and credit risk strategy.
• Design ML models for credit scoring, segmentation, and risk metrics.
• Build real-time decisioning pipelines across multiple markets.
• Develop and improve credit risk policies and thresholds.
• Establish MLOps standards for deployment and monitoring.
• Lead portfolio risk analytics, stress testing, and ECL modeling.
• Collaborate with finance, product, and operations to optimize profitability.
• Translate analytics into actionable executive insights.
• Drive experimentation, A/B testing, and data-driven product optimization.
• Scale partnerships with data providers and credit bureaus.
• Recruit, mentor, and develop a high-performing team.

Conhecimentos

Data science

Machine learning

Credit risk

Leadership

SQL

Cloud platforms

MLOps

Ferramentas

MLflow

BigQuery

Descrição da oferta de emprego

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Head of Data Science & Credit Risk based in Portugal.

This is a high-impact leadership role at the intersection of machine learning, credit risk, and financial inclusion, where you will define and scale the core decisioning systems that power lending across multiple Southeast Asian markets. You will own the end-to-end credit intelligence strategy, from model development and deployment to portfolio performance and business outcomes. In this role, you will lead a multidisciplinary team of data scientists and risk analysts, building advanced ML-driven underwriting systems that balance growth, profitability, and risk discipline. You will operate as a key strategic partner to senior leadership, shaping credit policy, risk appetite, and expansion strategy. The environment is fast-paced, data-driven, and mission-oriented, with strong emphasis on experimentation, scalability, and real-world impact. This is a unique opportunity to build credit risk and machine learning capabilities from the ground up in a rapidly scaling fintech.

Accountabilities:

• Define and lead the end-to-end data science and credit risk strategy, including underwriting models, portfolio risk frameworks, and decisioning systems.
• Design and deploy advanced machine learning models for credit scoring, fraud detection, segmentation, and customer value optimization.
• Build real-time and near-real-time decisioning pipelines supporting scalable credit underwriting across multiple markets.
• Develop and continuously improve credit risk policies, approval strategies, and risk thresholds aligned with business growth and portfolio health.
• Establish MLOps standards for model deployment, monitoring, versioning, and performance tracking in production environments.
• Lead portfolio risk analytics, including stress testing, expected credit loss modeling, and early warning systems for deterioration.
• Partner with finance, product, and operations teams to optimize unit economics, provisioning strategies, and capital allocation.
• Translate complex analytical outputs into clear, actionable insights for executive leadership and board-level discussions.
• Drive experimentation culture, including A/B testing frameworks and data-driven product optimization.
• Build and scale partnerships with external data providers, credit bureaus, and alternative data ecosystems.
• Recruit, mentor, and develop a high-performing team of data scientists and risk analysts.

Requirements:

• 10+ years of experience in data science, machine learning, and consumer credit risk within fintech, digital lending, BNPL, or EWA environments.
• Proven track record of building, deploying, and maintaining production ML models within real-time or near-real-time decisioning systems.
• Strong experience managing credit portfolios and designing credit policies across single or multi-market environments.
• Deep expertise in statistical modeling, machine learning techniques, and large-scale data analysis.
• Strong SQL skills and hands-on experience with cloud-based data platforms (e.g., GCP, BigQuery, or equivalent).
• Demonstrated leadership experience building and scaling technical teams while remaining technically engaged.
• Ability to communicate complex technical and analytical concepts clearly to business and executive stakeholders.
• Strong understanding of experimentation frameworks such as A/B testing and causal inference approaches.
• Experience in fintech risk, underwriting systems, or lending products with strong business impact.
• Familiarity with Southeast Asian credit markets, alternative data sources, or regulatory frameworks is a strong plus.
• Experience with MLOps tooling (e.g., MLflow or similar platforms) is highly desirable.
• Competitive salary package aligned with experience and market standards
• Equity participation in a high-growth fintech scale-up
• Opportunity to build credit risk and ML capabilities from the ground up
• High-impact role directly contributing to financial inclusion across emerging markets
• Fast-paced, mission-driven environment with strong ownership and autonomy
• Access to modern ML stack and cloud-native infrastructure
• Strong career progression opportunities in a rapidly expanding organization
• Culture focused on experimentation, innovation, and data-driven decision-making

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