Deskripsi Pekerjaan
Are you a seasoned Data Scientist looking to make a massive impact in the fintech ecosystem? GoTo Financial is seeking a Lead Data Scientist to join our high-performing Risk team in Singapore. In this pivotal role, you will be at the forefront of financial innovation, architecting robust machine learning solutions that safeguard our platform while enhancing user experience.
We are looking for a technical leader who thrives on complexity. You will not only be hands-on with model development but also mentor junior team members and collaborate with cross-functional product and engineering teams to bridge the gap between advanced data science and scalable production systems.
If you are passionate about building production-grade ML systems for credit scoring, fraud detection, and financial risk mitigation, we want to hear from you.
Tanggung Jawab
- Architect, develop, and deploy end-to-end machine learning models to solve complex financial risk problems.
- Collaborate with product and engineering teams to integrate ML solutions into our core financial services platform.
- Debug and troubleshoot high-stakes production ML systems to ensure model stability and performance.
- Perform deep-dive analysis on user behavior data to identify fraud patterns and credit risk trends.
- Mentor and coach junior data scientists, fostering a culture of technical excellence and continuous improvement.
- Optimize model training pipelines to increase efficiency and decrease latency in decision-making engines.
- Stay abreast of industry trends in fintech and artificial intelligence to drive innovation in our risk strategies.
Kualifikasi
- Master’s or PhD degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
- 5+ years of professional experience in data science, specifically focusing on credit risk or financial fraud detection.
- Expert proficiency in Python or R, and deep experience with ML libraries like XGBoost, LightGBM, or PyTorch.
- Strong experience with cloud infrastructure (AWS/GCP/Azure) and MLOps practices for model deployment (e.g., Docker, Kubernetes).
- Proven ability to write clean, maintainable, and production-ready code.
- Strong background in SQL and experience handling large-scale datasets using big data technologies (Spark, Hadoop).
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.