Data Scientist - Pricing

It's fun to work in a company where people truly BELIEVE in what they're doing!

Job Description

Our data science team is responsible for solving complex problems and optimizing various aspects of Traveloka's business. You will get a wide range of exciting opportunities to bring impact by helping to make decisions at scale across our various business lines, and get to work with the brightest minds in the industry. Join us in revolutionizing the travel industry through data!

Key Responsibilities:

• Collaborate with multiple business units to understand their challenges and goals.
• Translate business goals into technical requirements and actionable insights
• Develop, implement, and maintain data-driven solutions and systems
• Oversee and manage multiple data science projects, ensuring they meet the business objectives and are delivered on time.

Requirements

Qualifications

• Bachelor's degree in Computer Science, Data Science, Statistics, or a related field. Master's degree or higher preferred.

• Minimum of 3-5 years experience in data science or a related role.

• Excellent understanding of business operations and the ability to translate business requirements into technical specifications.

• Experience/familiarity with some of the followings:

  • Causal Inference

  • Econometrics

  • Simulation

  • Optimization Techniques

• Strong expertise in Python and experience with ML/Stats libraries (scikit-learn, LGBM, XGBoost to name a few).

• Proficient in data manipulation and analysis using SQL.

• Experience with Cloud-based tech stack and Docker. GCP will be preferred.

• Proven experience in managing data projects from inception to completion.

• Exceptional interpersonal and communication skills, including the ability to describe complex technical concepts to non-technical stakeholders.

• Proactive, self-motivated, and able to work independently.

Good to Have

• Experience in deep learning frameworks like PyTorch or TensorFlow.

• Experience with A/B testing and experimentation platforms.

• Experience with building and deploying machine learning models in a production environment.