Data Scientist
Tripledot Studios
| Company | Tripledot Studios |
| Category | Data & Analytics |
| Location | Jakarta |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 28 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
Who are we?
Tripledot Studios is one of the largest independent mobile games companies in the world.
We are a multi-award-winning organisation, with a global 2,500+ strong team across 12 studios.
Our expanded portfolio includes some of the biggest titles in mobile gaming, collectively reaching top chart positions around the world and engaging over 25 million daily active users.
Tripledot’s guiding principle is that when people love what they do, what they do will be loved by others.
We’re building a company we’re proud of. One filled with driven, incredibly smart and detail-orientated people, who LOVE making games.
Our ambition is to be the most successful games company in the world, and we’re just getting started. The role is working within our studio: Tripledot Games
About Tripledot Games
Tripledot Games is a leading developer and publisher of casual and puzzle games, with a strong presence in London, Warsaw, and Barcelona. The studio is responsible for multiple top 10 titles, including Woodoku, Woodoku Blast, Solitaire, and TripleTile. Our best-in-class data-driven approach spans the entire lifecycle from publishing to machine learning puzzle levels, helping us make fast, smart decisions at every stage of development.
We are also one of the largest IAA operators worldwide. Tripledot Games is a diverse and collaborative studio, home to people from over 36 nationalities. We take pride in our craftsmanship, strong focus on outcomes, and continuous improvement, building high-quality, scalable games that players around the world love.
Role Overview
As a Data Scientist in our Experimentation team, you will help deliver and improve Tripledot’s company-wide experimentation analysis platform. Working closely with Data Engineering, you will define the statistical logic and methodologies behind platform features, build prototypes in Python, and ensure the platform produces accurate, reliable results for product teams across our games portfolio.
Your initial focus will include hands-on quality assurance: running independent analyses, comparing results with platform outputs, investigating discrepancies, and validating features and metrics. This offers the opportunity to develop deep expertise in experimentation and A/B testing across a sophisticated internal platform. As the platform evolves, the role will expand into more advanced statistical and machine learning work that delivers value across the business.
Key Responsibilities
Define statistical logic, calculation methods, and detailed requirements for experimentation platform features.
Prototype statistical methodologies and analyses in Python before implementation.
Partner closely with Data Engineers as they develop and improve the experimentation platform.
Perform thorough data and feature QA by independently calculating results and comparing them with platform outputs.
Investigate discrepancies, identify root causes, and help ensure metrics and analyses are accurate and reliable.
Explore opportunities to automate QA processes and improve the efficiency and quality of validation.
Build a deep understanding of experimentation methodologies, including A/B testing, monitoring, segmentation, and analysis.
Collaborate with BI, Machine Learning, product teams, and other stakeholders to understand requests and shape effective solutions.
Contribute to future statistical, algorithmic, and machine learning initiatives as the team’s priorities evolve.
Skills, Knowledge and Expertise
3-5 years of professional experience as a Data Scientist or in a closely related role
Strong Python skills, including the ability to prototype analyses and write clear, reliable scripts.
Solid knowledge of statistics and standard statistical methodologies.
Practical understanding of experimentation and A/B testing, including how metrics and results should be calculated and validated.
Famili
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