Background
Nikel is an embedded SME lender. We provide end-to-end lending solutions to banks and technology companies in Southeast Asia. We believe the only way to solve the small business credit gap is by making lending more affordable. At Nikel, we offer the borrowers the ability to easily configure whatever type of loan is needed, by using our APIs to embed the solution within Borrowers' existing offerings and benefit from Nikel analytics to power robust credit decisions.
About the Role
The Company is recruiting for the Data Scientist role to join our Data Science & Risk Analytics Team (exact job title commensurate with experience), you will play a key role in leveraging data-driven insights to drive business decisions and enhance our products and services. You will work closely with cross-functional teams to develop and deploy data science solutions, with a focus on credit risk management, fraud detection, and customer analytics within the financial industry. This is an exciting opportunity to apply your analytical skills and creativity to solve complex problems and make a meaningful impact in the banking sector. You will work at all phases of the data science life cycle, including:
Build machine learning models through all phases of development, from design through training, evaluation, and validation, and partner with engineering teams to improve operationalization in scalable and resilient production systems to serve millions of customers.Partner closely with various business and product teams to conduct the experiments that guide improvements to customer experiences and business outcomes in domains like credit risk, marketing, servicing, and fraud prevention.Write software (Python, SQL e.g.) to collect, explore, visualize, and analyze numerical and textual data (customer transactions, payments, etc.) The ideal candidate will be:Curious and creative: You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find the answers. You are not afraid to share a new idea. You communicate clearly and effectively to share your findings with non-technical audiences.Technical: You have hands-on experience developing data science solutions from concept to production using open-source tools and modern cloud computing platforms. You are not afraid of petabytes of data.Statistically minded: You have built models, validated them, and backtested them. You know how to interpret a confusion matrix or an ROC curve. You have experience with clustering, classification, sentiment analysis, time series analysis, and deep learning.Customer and product-oriented: You share our passion for improving banking services. You understand the importance of customer satisfaction and product innovation in the banking industry. You are committed to using data-driven insights to enhance the customer experience, optimize product offerings, and mitigate credit risk effectively.Basic QualificationsMaster's or Bachelor's Degree in data analytics, statistics, computer science, mathematics, engineering, or related field.Good theoretical understanding of relevant statistical models, their inner workings, assumptions, and limitations.Experienced in developing credit/fraud models in the Retail/SME space within the Financial Industry, preferably in credit cards, retail/SME, payment, merchant analytics, application/behavioral/collections is a plus.Strong hands-on experience in the model development lifecycle for data scientists (training, testing, tuning, and performance monitoring).Excellent hands-on knowledge in Python and SQL.Ability to understand existing models and conduct customization to fit into local markets/data.A strong understanding of credit risk, data management, and data infrastructure is a plus.Comfortable in a fluid environment, self-directed, flexible, and creative.Commitment to the firm's social mission and corporate values.At Nikel, we are committed to diversity and equitable access to employment opportunities based on ability. We thank all applicants for their interest but will only contact candidates selected to advance in the hiring process.