資料工程師 Lead Data Scientist for Credit Automation (remote; Taiwan)

Updated about 2 months ago

Job Description

About The Team

Fazz’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 Fazz, 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 on and apply data science techniques to create solutions to 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 build the solutions.
    • You will work closely with other data scientists, and other members of the data, engineering and product teams to develop data science solutions for automating and improving credit application, disbursement and collection processes. These solutions will often take the form of machine learning models that are deployed to production, but may at times be delivered in different ways. Some tasks that you will work on include:
    • Researching on the latest machine learning approaches and technologies to automate processes in credit application, disbursement and collection. This may include work in domains of credit underwriting, computer vision, natural language processing, graph analytics, etc.
    • Providing thought leadership in the company on applying machine learning to credit automation
    • Work with business and product units to design and run experiments, so as to collect data for improving the team’s models
    • Work with machine learning engineers and software engineers to integrate models developed by the team to existing production systems the fraud detection solutions to existing production systems, working in collaboration with machine learning engineers and software engineers
    • Working with machine learning engineers to create mechanisms for tracking the solutions’ online performance

Requirements

What we’re looking for
    • BS/MS Degree or above in Math, Statistics, Computer Science, or Engineering
    • Hands-on experience with machine learning model development in python, preferably have deployed and maintained such models in production
    • Experience and deep understanding of SQL, Python programming, Machine learning models such as random forests, gradient-boosted trees, neural networks and/or graph analytics
    • Thrive in a collaborative environment


Nice to haves (Optional)
    • Relevant experience in Payments, Retail or Consumer Financial Services
    • Familiar with Google Cloud Platform

Interview process

Please provide English CV/Resume attachment for further process.

1
3 years of experience required
120K ~ 180K TWD / month
Managing staff numbers: not specified
100% Remote Work
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Logo of FAZZ - 新加坡商迅星金融科技有限公司台灣分公司.

About us

~~~Welcome to follow us for more updated information and job openings!~~~

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). Fazz was 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, under the brand Fazz Agen for Warung and MSMEs in Indonesia and Fazz Business for fast-growing startups and SMEs in Singapore & Indonesia.

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.


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