Fazz is an ecosystem of financial services that comprise Fazz (Business account for Southeast Asia), StraitsX (Payments infrastructure for digital assets), and Modal Rakyat (Mutual cooperation funding for MSMEs) founded in 2016, as a result of a merger between PayFazz and Xfers, two Y Combinator alumni based in Southeast Asia.
Fazz provides business accounts that offer seamless payment, savings and credit functionalities, giving businesses equal opportunity to build, run, and grow. We cater to the warung and MSME in Indonesia under the brand Fazz Agen, and fast-growing startups and SMEs in Singapore and Indonesia under the brand Fazz Business.
Fazz's mission is to make the future of finances accessible for every single business in Southeast Asia, where many MSMEs and the population are still underserved.
Head to our website to get to know us better:
About the Team
FFG's newly-formed data science team focuses on leveraging data to create solutions for an inclusive financial ecosystem in Southeast Asia to improve the lives of millions. In FFG, data science is the key to a safe and easy-to-use all-in-one financial platform for our users. The team's scope includes many aspects of the business, including areas related to customer onboarding, financial profiling, risk assessment, fraud detection and prevention, customer service, etc. We aim to build a platform that opens up a wide range of financial services to all our users while simultaneously protecting them as well as our business from the ensuing risks.
Roles and Responsibilities
- The team will research and apply data science techniques to create solutions for the challenges faced by the business. We will combine expertise from multiple disciplines of data science, including graph analytics, computer vision, anomaly detection, etc. to create our solutions. The machine learning engineer will work closely with other data scientists, data engineers, and software engineers to bring machine learning models to production. He/she will also help create platforms to make the process of developing and production rising such models more efficient.
- You will work closely with other data scientists and other members of the data, engineering, and product teams to productionize machine learning models. This role requires you to be familiar with the models created using different machine learning frameworks, and the different approaches that could be used to productionize such models given the use case. Some tasks that you will work on include:
- Work with stakeholders (product managers, software engineers, etc.) to understand how the machine learning model will be used in production, and develop an optimal approach to productionize the model
- Work with data scientists to deploy to production and continuously maintain their machine learning models, including developing retraining pipelines for the models
- Create monitoring systems to track the performance of machine learning models
- Collaborate with data engineering teams to create platforms to optimize the process of creating and productionizing machine learning models
- BS/MS Degree in Computer Science, Computer Engineering, or Engineering
- At least 1 year of experience in productionizing machine learning models, preferably on Cloud-based platforms
- Hands-on experience in software engineering using python
- Experience with DevOps or cloud platform technologies will be an advantage
- Thrive in a collaborative environment
- Hands-on experience building machine learning models
- Familiar with Google Cloud Platform and/or Amazon Web Services
- Relevant experience in Payments, Retail or Consumer Financial Services
Fazz is an equal-opportunity employer. Individuals seeking employment at Fazz will be considered without regard to race, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, physical or mental disability, or any other characteristic protected by applicable laws.
By submitting your application, you agree that Fazz may collect your personal data for recruiting, regional organization planning, and related purposes.
Fazz Financial