Background Nikel is a financial technology company that enables small business lending in Southeast Asia. We combine deep expertise in alternative lending with best-in-class technology solutions to bridge international capital markets with underserved communities in Southeast Asia. Website: https://nikel.com/ Role The Company is seeking a Data Scientist / Sr Data Scientist to join our Data Science & Risk Analytics Team (exact job title commensurate with experience), to help accelerate the Company's innovation and data-driven strategy, including data management, predictive analytics, machine learning, and intelligent automation. He or she should also have a strong understanding of credit and fraud in the SME segment. He or she will also be responsible for day-to-day risk management of the Company's loan portfolio and delivering output for external stakeholders, e.g., credit scoring tools for banks and articles for distribution to investors. 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 fintech and banking.Responsibilities Apply advanced analytical skills and machine learning experience to develop and implement credit models, fraud detection models, behavioral analytics, and optimization models.Build analytics dashboards for actionable insights and reporting solutionsEffectively communicate your projects to your internal and external stakeholders, including customers.Manage project timeline, including project planning, execution, implementation, monitoring, and reporting.Be constantly improving our practices and frameworks related to Data Science at a scale.Collaborate with the technology team on integrating and implementing the models into the products.Mentor and provide technical guidance to junior team members, as applicable.Basic Qualifications Master's/Bachelor's degree in a quantitative discipline (e.g., Mathematics, statistics, physics, engineering, computer science, data analytics, machine learning).Good theoretical understanding of relevant statistical models, their inner workings, assumptions, and limitations.2-5 years of experience developing credit models in the Retail/SME space, credit cards, merchant analytics, application/behavioral/collections, fraud detection models is a plus.Experience in the model development lifecycle for data scientists (training, testing, tuning, and performance monitoring) is a plus.Experience with data visualization and dashboard design; proficient in visualization tools (e.g., Tableau) is a plus.Excellent hands-on knowledge in at least 2 of these languages: Python, R, SQL.Experience with IRB, IFRS9, and local regulation for ASEAN countries is a plus.Understanding of data management and data infrastructure.Comfortable in a fluid environment, self-directed, flexible, and creative.Commitment to the firm's social mission and corporate values.Benefits Challenging role in a startup environment with strong growth ambitionsMeaningful work toward the Company's social impact mission and visionCompetitive compensation and benefitsAt Nikel, we are committed to diversity and equitable access to employment opportunities based on ability. Interested candidates can send their applications to [email protected]. We thank all applicants for their interest but will only contact candidates selected to advance in the hiring process.