Wego – Data Science Intern

About the Role

The Data Team supports Wego in a multitude of analytical projects and initiatives, with core functions ranging from data analytics, data engineering and data science. We also support all of our internal stakeholders, from product, marketing, finance, sales and operations, to help them uncover important business insights to make well-considered data-driven decisions.

This internship role is working with the Lead of Data Science and Innovation residing within the core Data Team to help support the development of new data science and analytical initiatives. These initiatives help our company better analyse and understand the customer user journey and their travel intentions, to better curate better optimisations and user experience on our travel platform. This role would also be closely attached to our Product Manager stakeholders (Flights or Hotels), who would want analyses, KPI dashboards and ML model implementations for their product-related Data Science use cases.

Responsibilities

• Help out in the development and maintenance of advanced analytics and new data science initiatives, mainly on recommended sort order, rate cache optimisation or experimentation with A/B testing. Other longer term keystone projects may involve, personalisation, predictive customer lifetime value, travel persona segmentation and propensity models.
• Analyse our datasets to uncover key insights and recommendations through the use of effective data analysis and visualisations.
• Assist in building out new analyses, feature engineer for ML models and develop ETL/ELT processing scripts.
• Investigate potential data issues and perform exploratory data analysis in verifying certain hypotheses.
• Understand business/technical use cases of our Product Managers (Flights or Hotels), to be able to help investigate certain hypotheses and communicate insights to them.
• Writing out and documenting our approaches, to support the communication and presentation of data science solutions to stakeholders. 
• Capacity to look through data science research and industry white papers to consolidate and identify best practices/approaches that we can practically apply in certain areas.
• Help support and monitor our existing and new statistical/ML solutions, in evaluating KPIs/success metrics.

Requirements 

• Aptitude or inclination towards mathematics/statistics, with an adaptable coding ability (i.e. Python).
• Good ability in using more complex SQL functions (i.e. windowing, DDL, DML syntax). Be able to understand lengthier, nested queries and visualise what each section is specifically doing to deliver the precise calculation that you want.
• More inclined to work with disparate kinds of datasets, to independently figure out how best to manipulate them to arrive at insights.
• Know how to generally develop dashboards (i.e. Looker, PowerBI, Tableau) to showcase pertinent insights. Not necessary for specific visualization tool knowledge.
• Able to communicate effectively, on systematically breaking down a problem, articulating/distilling key issues and brainstorming solutions, especially as we deal with more complex data science problems and considerations.
• Keen on supporting the development of new areas of analyses, as at times, we are trying to attempt, introduce and convince internal stakeholders of new analytical approaches/KPIs.
• Proactive and adaptable in the face of unknowns. Good troubleshooting ability in dealing with problems.
• Have a general understanding/inclination of how data science models/statistical approaches work (i.e. Neural Networks, Random Forests, Linear Regression, Principal Component Analysis, K Means), hyperparameter tuning and model evaluation metrics (i.e. Accuracy, Confusion Matrix). 

Similar experience in the following tools that we use would be good, but not mandatory:

• Google BigQuery
• Google BQML/Vertex AI
• Looker
• Tableau
• Python