About the Job

Our Experiments team builds growth engines for Xendit through experimental products. We do this by pushing the frontiers of what is possible in the industry and creating modern solutions to old problems. As part of the Experiments team, you may become part of a team that builds from 0 to 1, from 1 to 1,000, or from 1,000 to the moon. It requires flexibility and adaptability to take on new challenges in order to grow an early-stage product.

To build from the ground up, we take ideas, customer pain points, or gaps in the market, push out an MVP at speed and iterate to find product-market fit. This team presents a unique opportunity for people who are looking to build something new, rather than scaling mature products. People who join this team have a strong interest in growth (personally and professionally) and, therefore, a continuous improvement mindset.

We're looking for a passionate individual who is willing to do whatever it takes to help us build credit models for innovative products that our local market has not seen before.

Minimum Qualifications

• Minimum Bachelor's Degree in a quantitative field (i.e). Computer Science, Mathematics, Statistics, Econometrics, Engineering, or Artificial Intelligence.
• 3+ years of work experience with Python and SQL programming for data analysis
• 3+ years of work experience in data science
• Solid oral and written communication skills, especially around analytical concepts and methods.
• Capability to work effectively under pressure, meeting tight deadlines with proactive, decisive, and adaptable approaches.
• Strong passion for data-driven problem solving.
• Familiarity with credit risk models and financial modelling is a plus.

Preferred Qualifications

• 3+ years of work experience with Machine Learning or Statistical Modeling
• Working experience with AWS
• Experience managing projects independently
• Self-motivated with intellectual curiosity
• Proven ability to quickly learn new technologies, concepts and tools

Responsibilities

1. Modeling independently
1. Collaborate with business stakeholders to understand new business initiatives.
2. Design and develop models as standalone products.
3. Collect, process, and transform data.
4. Investigate new data sources.
5. Build, demonstrate, measure, and optimise performance end-to-end modeling solutions.
2. Analytical Problem Solving:
1. Convert vague contexts and phenomena into structured analytical problems.
2. Utilize statistical knowledge, machine learning models, and visualization techniques to extract meaningful insights from data related to business goals and strategies.
3. Design and conduct experiments and A/B testing for measuring business initiatives.
4. Perform regular testing analysis.
3. Innovation:
1. Embrace and adapt to rapid technological and scientific advancements.
2. Explore advanced tools and information sources to aid the team in understanding and solving problems and maintaining products