Data Scientist
Job description
About the role
This role operates within the Tripledot Games studio in the Experimentation team. You will strengthen company-wide experimentation analysis by defining methodologies, validating outputs, and supporting platform evolution. The position blends statistical design, data validation, and collaboration to serve product decisions across a global mobile games portfolio.
Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.
Key facts
What you'll do
Python prototypes validate concepts and support implementation by building clear, reliable scripts before production development.
Collaboration with Data Engineers refines and improves the experimentation platform through coordinated requirements, design, and delivery.
Independent calculations perform thorough data and feature quality assurance by comparing results with platform outputs to confirm correctness.
Root cause analysis investigates discrepancies between independent analysis and platform results to ensure metrics and analyses remain accurate and reliable.
Automation exploration identifies opportunities to improve QA efficiency and creates scalable checks for experimentation workflows.
Requirements
3-5 years of professional experience as a Data Scientist or in a closely related role, with consistent delivery in analytical contexts.
Strong Python skills enable prototyping analyses and writing clear, reliable scripts that support experimentation validation.
Solid knowledge of statistics and standard statistical methodologies underpins measurement and evaluation.
Practical understanding of experimentation and A/B testing includes how metrics and results should be calculated and validated in digital products.
Familiarity with machine learning methods allows relevant approaches when analytical needs require it.
Detail orientation sustains accuracy through repetitive or methodical QA work across large datasets.
Problem-solving skills drive creative solutions and process improvements for experimentation workflows.
Clear communication explains analytical methods, results, and rationale to both technical and non-technical audiences.
Contextual experience with experimentation platforms or high-volume digital products, such as gaming, e-commerce, or ride-hailing, adds valuable perspective.
Critical evaluation assesses and validates AI-generated code, analyses, or modelling suggestions to ensure correctness and reproducibility in production.
AI-assisted tool experience uses code assistants or analytical copilots to accelerate exploration, research prototyping, and development while maintaining scientific and statistical rigor.
Interest in AI-driven approaches seeks improvements for experimentation platforms, developer productivity, or data science research workflows in a games environment.
Nice to have
Experience in gaming, IAA, or high-volume digital products where experimentation and A/B testing guide decision-making.
Practical notes
This role is based in Jakarta with hybrid working, including 20 days of remote work per year and required office days. Employment terms follow local regulations, and role details are confirmed through the official apply page.
Typical interview steps
Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.
Good to know
Data Scientists in games companies use statistics and experimentation to guide product decisions and analyze player engagement. Python is widely used for analysis prototyping, data pipelines, and automation in this field. Experimentation platforms support high-volume digital products and demand rigorous validation to keep metric accuracy. Familiarity with AI-assisted coding tools can speed workflows while preserving scientific rigor. Continuous learning helps professionals adapt methods and tools as platforms evolve.