About the Role

We are seeking a Data Scientist who can translate complex data into practical, scalable solutions that drive business impact. This role is ideal for someone hands-on, curious, and confident in owning end-to-end projects—from data exploration to deploying production-ready models. You will collaborate closely with product, engineering, and business teams to understand challenges, guide decisions, and deliver measurable results.

You will contribute significantly to building and enhancing our machine learning ecosystem, partnering across the organisation to elevate how data is utilised. The work spans experimentation, model development, and deploying low-latency models in a cloud environment. If you enjoy solving real-world problems with real data and want to help shape a modern data function, this role will be a strong fit.

Location

Bali – In-office

Level

Senior (Experienced in the same field)

Responsibilities

Develop and implement machine learning models to solve real business challenges.
Analyse large and complex datasets to identify patterns, trends, and insights.
Collaborate with cross-functional teams to understand requirements and deliver clear, data-driven solutions.
Build and maintain reliable data pipelines while ensuring data accuracy and integrity.
Communicate findings and recommendations to both technical and non-technical stakeholders.
Stay updated with new tools, technologies, and methodologies in data science and machine learning.

Requirements

Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field.
Minimum four years of experience in data science or machine learning roles.
Strong programming skills in Python, R, or similar languages.
Hands-on experience with supervised/unsupervised learning, deep learning, and reinforcement learning.
Proficiency with visualisation tools such as Tableau, Power BI, or D3.js.
Strong analytical and problem-solving abilities.
Excellent communication skills and a collaborative working style.
Experience with distributed computing frameworks (Apache Spark, Hadoop, or Dask).
Experience with cloud platforms (AWS, GCP, or Azure).
Familiarity with software development practices: version control, code reviews, and testing.
Experience working with Databricks (primary data environment).
Experience deploying models that support low-latency inference (300–500 ms) with high-volume, variable data inputs.