Data Scientist

PT WEGO MEDIKA INDONESIA; Jakarta Pusat; On-site; IDR 350,000,000 - 550,000,000; Full time

Job summary

Wego is seeking a Data Scientist to build and refine pricing, bidding and conversion models across our metasearch marketplace and OTA products. You will lead causal analysis to separate effects of decisions from user and market shifts.

Responsibilities include designing experiments, estimating responses, and deploying production models with engineering teams while ensuring robust validation and clear communication of uncertainties.

Qualifications

• 3–8 years of experience in data science or applied statistics, with practical experience in causal inference.
• Strong skills in experimental and quasi-experimental design, probability calibration and model evaluation.
• Experience developing production models and collaborating with engineers on deployment and monitoring.
• Fluency in SQL and Python; ability to choose interpretable methods and explain assumptions.
• Experience with cloud platforms for data access, development and deployment.
• Familiarity with AI coding tools and maintaining analytical reproducibility.

Responsibilities

• Design experiments and quasi-experimental analyses to measure the effect of pricing, bidding and conversion decisions.
• Estimate price responses and tradeoffs to inform pricing and bidding with clear objectives.
• Build calibrated probabilistic models that remain robust with sparse data and changing conditions.
• Identify opportunities in product behavior and business performance and lead them to measurable outcomes.
• Work with engineers to ship models, monitor performance and validate results with stakeholders.
• Document assumptions, limitations and ensure analyses are reproducible.

Skills

SQL

Python

Causal inference

Experimental design

Bayesian modeling

Production models

Deployment monitoring

Cloud platforms

AI coding tools

Tools

Claude Code

Codex

Job description

We’re on a mission to help people discover the real value of travel — to inspire, to give more reasons, to make it easy — for you to go. Our company was founded back in 2005, and since then, we’ve imagined and created some of the most well-loved products for travelers all around the world.

Today, Wego is used by millions of people every month — people who travel for adventure, work, family, and for many other reasons. That’s why we work tirelessly to make your experience of planning & booking flights, hotels, and trips as seamless as possible.

Team Description:

You’ll join Wego's Data Analytics & Data Science team, which works across our Flights and Hotels products, from the metasearch marketplace and ad units to our OTA business, in markets worldwide.

Our roadmap has more high-impact projects than ever, and we're growing the team to deliver the core Flights and Hotels work that needs strong data science and statistical skills.

The Role:

You will build statistical models that guide pricing, bidding and conversion decisions across Wego's metasearch marketplace and our hotel and flight products. Causal inference is central to the role: you'll separate the effect of a decision from changes in users, markets and measurement.

We value modeling experience from risk, insurance, actuarial or other fields where calibrated probabilities and uncertainty affect real decisions. You'll own the statistical reasoning, validation and interpretation, and partner with engineering to put models into production.

What you will be working on:

Designing experiments and quasi-experimental analyses that show the real effect of pricing, bidding and conversion decisions

Estimating price response and gain/loss tradeoffs, and turning them into pricing and bidding decisions with clear objectives and constraints

Building calibrated probability models that hold up under sparse data and changing conditions

Finding opportunities in product behavior and business performance, and carrying them through to an evaluated outcome

Core Responsibilities:

Make identification assumptions explicit, test their plausibility and explain what the evidence supports

Quantify uncertainty using scenario analysis and sensitivity checks

Compare methods against useful baselines and check calibration, bias and stability across segments

Distinguish predictive performance from evidence about the effect of a decision

Partner with engineers to ship models, define monitoring and investigate performance changes

Document assumptions and limitations so others can use models correctly

Validate the value of new opportunities with stakeholders and define success upfront

Help analysts and product teams frame testable questions, and explain uncertainty and tradeoffs in practical terms

Skills and experience:

3–8 years of experience in data science, applied statistics or a related field, with practical experience in causal inference

Strong grounding in experimental and quasi-experimental design, probability calibration and statistical model evaluation

A record of using models to inform decisions and evaluate their impact

Experience developing models for production and working with engineers on deployment and monitoring

Fluency in SQL and Python

Practical experience with causal inference and Bayesian modeling, including confounding, selection effects and limited data

Ability to choose interpretable statistical methods that fit the problem and explain their assumptions

Experience with cloud platforms for data access, model development and deployment

Fluency with AI coding and analysis tools, such as Claude Code or Codex, combined with independent statistical judgment

You spot worthwhile problems without waiting to be assigned, validate why they matter and own the solution through to measurable impact

You align priorities with stakeholders, then work independently through ambiguity and missing context

You use AI fluently but own the output: you check generated analysis against the data and challenge a convincing result when the evidence doesn't support it

You make analyses reproducible and own defects in the models and conclusions you deliver

You communicate with precision, share knowledge and learn new domains quickly

Additional Info:

Experience with risk or actuarial modeling, insurance analytics or comparable work involving uncertainty and decision consequences is a strong plus

Travel, pricing, marketplace, ranking or personalization experience is a plus