Data Scientist/Senior Analyst
Job description
Senior Data Analyst at Agoda.
About the role
This position operates from Bangkok, Thailand and reports to a Senior Manager or Associate Director in Supply. The role translates business briefs into analytical work, partners with stakeholders, and delivers data-driven recommendations to improve supply decisions.
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
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Location: Thailand
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Engagement: Individual contributor
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Team: Supply Analytics
- Years: 5+
- Degree: Bachelor's degree in a business or quantitative subject
What you'll do
Large-scale warehouse data is analyzed to support business stakeholders. Supply and wider business opportunities are identified proactively. Internal briefs are transformed into analytical projects, with questions refined and hypotheses explored. Time savings from automating manual operational processes are presented back to the business. Initiatives that scale how Supply uses data, insights, and intelligence are led and supported.
Requirements
At least 2-5+ years of experience as an Analyst in analytics, data science, insights, strategy, or BI is required. Advanced working knowledge and hands-on experience in SQL are necessary. Strong knowledge and hands-on experience in data visualization tools such as Tableau are required. Expert domain knowledge in data analysis and data visualization software such as Excel, Python, or R is required. A Bachelor's degree ideally in a business or quantitative subject such as computer science, mathematics, engineering, science, economics, or finance is required. A good understanding of statistical modeling and machine learning techniques such as hypothesis testing, regression, logistic regression, random forest, and experience conducting A/B testing experimentation is required. Stakeholder management, presentation, and communication skills for senior audiences must be demonstrated. A data-driven approach in decision making and performance measurement is required. Comfort in ambiguous, fast-paced environments is required. The ability to multi-task, prioritize, and coordinate resources effectively is required.
Nice to have
An MBA or Masters in a quantitative subject, program management certifications such as PMI or PRINCE2, Asian market experience, and travel industry, e-commerce, tech, or consulting experience.
Skills & tools
Advanced SQL, data visualization tools such as Tableau, Excel, Python or R, and experimentation methods such as A/B testing.
Practical notes
The position is not open for remote work. 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 science roles in travel e-commerce often combine analysis, experimentation, and stakeholder influence. Professionals in this field commonly use SQL, Python or R, and visualization tools to turn data into decisions. Clear communication and structured storytelling are essential when presenting to leadership. Modern data teams value automation that improves operational efficiency and uncovers scalable insights. Analytical rigor, business context, and experimentation help shape product and supply strategies.
Career growth
Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.