About Finantier

At Finantier we are building the infrastructure for the next generation of Financial Services across Southeast Asia. We are a VC backed startup creating from scratch an holistic platform to allow consumers to control and leverage on their data to access better financial products and empowering innovators to build and scale amazing tailored solutions for their users.

About the role

About us

At Finantier we are building the infrastructure for the next generation of Financial Services across Southeast Asia.

We are a VC backed startup creating from scratch an holistic platform to allow consumers to control and leverage on their data to access better financial products and empowering innovators to build and scale amazing tailored solutions for their users.

Our team is small but growing fast, there’s a lot of room for career growth and also a lot to have fun along the way.

Responsibilities

As a Senior Data Scientist you’ll have the opportunity to work in the following items, among others:

Utilize big data and machine learning to build predictive models including but not limited to customer acquisition, credit risk, fraud, financial insights, among others.
Work with multiple, complex data sources (financial, psychometric, transactional etc.) at large scale.
Perform thorough testing and validation of models and support various aspects of the business with data analytics.
Evaluate available data sources and propose strategic acquisition or creation of new data sources to assist ongoing problem resolution.Identify new data sources/patterns that add significant lift to predictive modelling capabilities.
Collaborate across multiple teams to address their needs in terms of compliance, product enhancements and performance.
Maintain internal and public documentation about our products and services.
Have fun and enjoy what you do.

You are

Highly motivated and self driven.
Enjoy collaborating and working in small teams and cross teams.
Technically strong and hands-on.
Good at multitasking and thrive in fast-paced environments.
Methodical, thorough, and solution oriented.
Enjoy learning new technologies and applying that to solving problems.
Great at communicating your ideas.

Requirements

3+ years of working experience with data mining, statistical analyses, and model building/machine learning applications
Strong understanding of both traditional statistical methods (e.g., linear regression) as well as more modern data science models (e.g., gradient boosted machines) and their respective tradeoffs.
Excellent theoretical foundation in statistics, probability/mathematics
Programming fluency in Python or R or similar scripting language
Prior experience working with data at a decent scale.
Is a plus if you have prior startup experience or if you worked in a Fintech or SaaS company.
Also important if you have past experience in alternative credit bureau design and building solutions for financial institutions.
Is another plus if you had the chance to work with highly scalable distributed systems.

Tech Stack

We use Python, Golang and Rust for backend in a microservices architecture, we also rely on serverless processes in the cloud. We also use Docker and Kubernetes to deploy our infrastructure. ReactJS is our library for the frontend.

As we continue building and evolving our platform, be ready to get up to speed with new technologies and exciting tools.

Perks

Real ownership of what you do.
Work from home with some periodic office time.
When COVID allows, ability to travel within the region.
Training budget.
Macbook.
15 days vacation + public holidays.
Flexible working hours.
Being part of an awesome team, making a real positive impact on people’s lives.

Technology

We use Python, Golang and Rust for backend in a microservices architecture, we also rely on serverless processes in the cloud. We also use Docker and Kubernetes to deploy our infrastructure. ReactJS is our library for the frontend.

As we continue building and evolving our platform, be ready to get up to speed with new technologies and exciting tools.

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