Role Overview:
We are hiring a Head of Data Science for a fast-growing fintech product organization focused on building advanced credit intelligence, risk scoring, and embedded financial solutions.
This is a senior leadership role responsible for defining and driving the end-to-end data science strategy, with strong ownership of credit risk modeling, predictive analytics, fraud detection systems, and scalable ML platforms.
You will lead a high-impact data science function and work closely with engineering, product, and external financial partners to enable data-driven lending and financial decisioning at scale.
Key Responsibilities
- Define and lead the data science and AI strategy aligned with business and credit risk goals
- Own development of credit risk scoring models, underwriting frameworks, and decision systems
- Build and deploy predictive models for credit risk, fraud detection, and portfolio optimization
- Collaborate with external partners (banks, NBFCs, fintechs, telecoms, etc.) on model development, data integration, and evaluation
- Work closely with data engineering and software engineering teams to build scalable data pipelines and ML infrastructure
- Drive model governance, validation, monitoring, and performance optimization
- Translate complex analytical insights into clear business recommendations for leadership teams
- Develop dashboards and reporting systems for credit performance, risk exposure, and portfolio insights
- Lead, mentor, and grow a high-performing data science team
- Stay updated with emerging trends in AI, ML, credit risk modeling, and fintech innovation
- Drive experimentation, innovation, and continuous improvement across data science initiatives
What We’re Looking For
- Master’s or PhD in Computer Science, Statistics, Mathematics, or related field
- 8–12+ years of experience in Data Science / Machine Learning roles
- Strong expertise in credit risk modeling, lending analytics, or financial services data science
- Proven experience in leading or managing data science teams
- Strong programming skills in Python / R and SQL
- Deep understanding of ML techniques including:
- Logistic Regression
- Decision Trees / Random Forests
- Gradient Boosting (XGBoost / LightGBM / CatBoost)
- Neural Networks (good to have)
- Hands-on experience in model deployment, MLOps, CI/CD pipelines, and model monitoring
- Strong understanding of credit data, bureau data, and alternative data sources
- Experience working with cross-functional teams and external stakeholders
- Strong business acumen with ability to connect data science outputs to business impact
- Excellent communication and stakeholder management skills
- Highly hands-on, execution-focused leadership style
- GitHub, research, or open-source contributions will be an advantage
