数据科学与信用风险主管
Head Of Data Science & Credit Risk
职位名称:数据科学与信用风险负责人
部门:数据科学与信用风险
地点:全球
关于 FINN
FINN 是一家金融科技公司,致力于打造简单透明的产品,帮助缺乏银行服务的员工改善财务状况。我们的核心产品让人们可以获取已经赚取的工资,同时提供工具帮助他们更好地管理资金、建立储蓄、获得保险、赚取额外收入,并使用更公平的金融服务。
FINN 于 2022 年成立,是东南亚早期工资预支领域的最大玩家,正在迅速扩展到全球新市场。我们寻找有抱负的建设者,他们具备主人翁意识,行动迅速,并希望打造能对人们生活产生深远影响的产品。
我们正在寻找一位数据科学与信用风险负责人,领导我们的机器学习驱动的信用评估策略,并加强整个东南亚地区的风险决策能力。在这个职位上,你将结合深厚的数据科学专业知识与信用风险管理领导力,负责从模型开发到业务影响的全流程。你将组建并领导数据科学家和风险分析师团队,与工程、产品和财务紧密合作,优化审批流程,提升组合表现,并负责任地扩大信贷可及性。
机器学习与模型开发
- 领导信用评估、欺诈检测和风险分组的机器学习模型的设计、测试和部署。
- 开发使用替代数据源的信用评估算法,以提高风险评估能力并扩大金融可及性。
- 构建并部署实时或近实时评分模型,使其在多个市场中可扩展。
- 确保模型具有可解释性、公平性和稳健性,并对准确性、特征稳定性及漂移进行监控。
- 建立 MLOps 实践,包括模型版本控制、实验、部署和持续监控。
- 推动机器学习在业务中的广泛应用,包括客户价值、变现和营销归因。
信用风险策略与监控
- 开发并管理适应各市场的信用风险框架、政策和审批策略。
- 设定风险阈值和客户分群策略,平衡增长、违约率和组合健康度。
- 监控关键风险指标,调查重大变化,并建立组合恶化早期预警信号。
- 模拟政策和模型变更,支持 A/B 测试,并利用绩效数据和业务关键指标优化策略。
查看英文原文
Position Title: Head of Data Science & Credit Risk
Department: Data Science & Credit Risk
Location: Global
About FINN
FINN is a fintech company building simple and transparent products that improve financial well-being for underbanked employees. Our core offering gives people access to wages they have already earned, alongside tools to help them manage money better, build savings, access insurance, earn additional income, and use fairer financial services.
Founded in 2022, FINN is the largest player in the early wage access space in Southeast Asia and is expanding rapidly into new markets globally. We are looking for ambitious builders who take ownership, move fast, and want to shape products that create meaningful impact in people’s lives.
We’re seeking a Head of Data Science & Credit Risk to lead our ML-driven underwriting strategy and strengthen risk decisioning across Southeast Asia. In this role, you’ll combine deep data science expertise with credit risk leadership, owning the full lifecycle from model development to business impact. You’ll build and lead a team of data scientists and risk analysts, working closely with engineering, product, and finance to improve approval funnels, strengthen portfolio performance, and expand access to credit responsibly.
ML & Model Development
- Lead the design, testing, and deployment of ML models for credit decisioning, fraud detection, and risk segmentation.
- Develop underwriting algorithms that use alternative data sources to improve risk assessment and expand financial access.
- Build and deploy real-time or near-real-time scoring models that scale across multiple markets.
- Ensure models are interpretable, fair, and robust, with monitoring for accuracy, feature stability, and drift.
- Establish MLOps practices for model versioning, experimentation, deployment, and ongoing monitoring.
- Expand machine learning adoption across the business, including customer value, monetization, and marketing attribution.
Credit Risk Strategy & Monitoring
- Develop and manage credit risk frameworks, policies, and approval strategies adapted to each market.
- Set risk thresholds and customer segmentation strategies that balance growth, default rates, and portfolio health.
- Monitor key risk metrics, investigate significant changes, and establish early warning signals for portfolio deterioration.
- Simulate policy and model changes, support A/B testing, and refine strategies using performance data and business KPIs.
- Lead stress testing and expected credit loss modeling, partnering with Finance on provisioning and capital allocation.
- Support market expansion through localized risk models and policies aligned with applicable regulatory requirements.
Team & Strategic Leadership
- Build, lead, and mentor a team of data scientists and risk analysts while remaining hands-on with technical work.
- Own the data science and credit risk roadmap, aligning priorities with business growth and expansion plans.
- Communicate model performance, portfolio trends, and strategic recommendations to the executive team and board.
- Partner with engineering, product, and finance to translate analytical insights into measurable business outcomes.
- Evaluate and establish partnerships with alternative data providers and credit bureaus.
- Build a culture of experimentation, accountability, and data-driven decision-making.
Business Impact
- Improve approval rates while maintaining target default rates and responsible lending standards.
- Reduce time-to-decision through automated underwriting and scoring.
- Identify new customer segments and product opportunities through advanced analytics.
- Improve unit economics through risk segmentation and customer value modeling.
- Track the impact of model and policy changes, using feedback loops to improve performance over time.
Your Profile
Required:
- At least 10 years of combined experience in data science, machine learning, and consumer credit risk within fintech, digital lending, BNPL, or earned wage access.
- Experience developing and managing credit policies and portfolios at scale across multiple products, markets, or both.
- A proven track record of building, deploying, and monitoring production ML models within real-time or near-real-time decisioning pipelines.
- Hands-on experience with experimentation and A/B testing to evaluate model and policy changes.
- Strong statistical and mathematical skills, with expertise in both classical statistical methods and modern machine learning.
- Strong working knowledge of SQL and exploratory data analysis, alongside experience with cloud data platforms. We use GCP BigQuery, but experience with this specific platform is not required.
- Experience building and leading technical teams while remaining actively involved in model development and problem-solving.
- Strong communication skills, with the ability to explain complex models and credit risk concepts clearly to business stakeholders.
- Sound business judgment and the ability to connect technical decisions to growth, portfolio performance, and return on investment.
- An adaptable, proactive approach to working in a fast-paced startup environment.
Bonus:
- Familiarity with Southeast Asian credit markets, credit bureaus, and alternative data sources.
- Experience with MLflow or similar frameworks for production model development and deployment.
- Understanding of IFRS 9 and local credit regulations across multiple markets or regions.
What We Offer
- A competitive salary based on experience and location.
- Equity participation.
- Opportunities to learn, grow, and shape a growing data science and credit risk function.
- The opportunity to help redefine financial services for underbanked employees.
Why Join FINN?
- Meaningful Impact: Build models that expand financial access for underbanked employees.
- Greenfield Opportunity: Shape our ML and risk capabilities from the ground up.
- Modern Technology: Work with a modern ML stack and cloud infrastructure.
- Rapid Growth: Lead a core function as FINN scales into new markets.
- Mission-Driven Work: Help improve financial well-being through fairer, more accessible financial services.
#Global
Originally posted on Himalayas