Machine Learning Engineer

About the Role

The Kuala Lumpur office is the technology powerhouse of MoneyLion. We pride ourselves on innovative initiatives and thrive in a fast paced and challenging environment. Join our multicultural team of visionaries and industry rebels in disrupting the traditional finance industry!

We are looking for a Machine Learning Engineer who is technically proficient in programming to build machine learning systems and maintain high performing machine learning models in production. Designing machine learning systems requires that you, as the machine learning engineer, is able to frame business problems and design machine learning solutions that can effectively bring value to the company.  You will prototype, implement and experiment with machine learning methods to solve business use cases. You will lead efforts to automate, architect and optimise machine learning processes and workflows. You will also help define, execute and enforce best practices for test-driven model development processes for the entire Data Science team in MoneyLion.

Key Responsibilities

• Able to frame and contextualize machine learning problems
• Research, build and design machine learning systems and models that can solve business problems
• Assess, understand and analyse data to select appropriate datasets for data modelling and testing
• Identify, prototype and implement appropriate machine learning algorithms and tools to improve our products
• Design, execute and monitor machine learning model experiments and metrics
• Perform statistical analysis to evaluate and prove business impact of modelling improvements 
• Automate, architect and orchestrate machine learning processes and pipelines
• Monitor, optimise and maintain machine learning solutions in production
• Enrich existing machine learning libraries and model development frameworks to empower model development in MoneyLion
• Work with MLOps Engineers and Data Scientists to improve systems designs and architectures
• Enforce test-driven development and advocate engineering best practices to reduce technical debt in machine learning systems
• Develop tools and internal libraries to facilitate model governance over the machine learning development lifecycle

About You

• Strong mathematical, statistical or actuarial background
• Good programming knowledge and software engineering skills
• Must have hands on experience in machine learning, predictive analytics and statistical modelling
• Experience developing and deploying machine learning models in production.
• Adept at problem solving and troubleshooting using both textbook methods and novel viewpoints
• Proficient in Python and SQL
• Proficient with machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM
• Preferably have experience in building machine learning systems and workflows
• Preferably familiar and have experience with MLOps tools such as DVC, MLFlow, Metaflow, Seldon, BentoML
• Preferably familiar and have experience with AWS, Docker, Kubernetes.
• Solid communication and collaboration skills.

What's Next...

After you submit your application, you can expect the following steps in the recruitment process:

1. Online Technical Test
2. Take-Home Assessment
3. Interview & Discussion of Take-Home Assessment - Hiring Manager (Virtual or face-to-face)

*If you’ve already sent in your application for this position and were not selected, please reapply after 6 months.*