Data Scientist, Causal Inference

As the leading delivery company in the region, we have a great responsibility and opportunity to impact the lives of millions of customers, restaurant partners, and riders. To realize our potential, we need to advance our platform to become much more intelligent in how it understands and serves our users.

We are looking for a Data Scientist specialized in Causal Inference to join our growth team. Your mission will be to improve the quality of the decisions made across product and business via relevant, reliable, and actionable insights. You will be working closely with data scientists as well as product and business partners to answer challenging business questions. Examples of these questions:
- What happens to retention when we reduce/or increase delivery time?- How does the delivery charge impact our customers?- Should we issue an automated refund for orders that are more than 30 minutes late? How can we design a study to answer this research question?- How can we use statistical and causal inference tools to evaluate the long term effects of interventions?

The answers to these questions will improve our customer understanding, allow us to quantify the effect of interventions, and help the decision makers shape the strategy for Talabat.

What do we offer?

An opportunity to:
- Work on challenging problems.- Provide a real business impact using data.- Work on a massive amount of data that will feed your hungry mind.- Contribute to shaping the vision for customer retention data science.- A safe environment where you can learn and grow.- Work with great teams in a great work place.- A competitive package.

**Responsibilities**:
- Develop a deep understanding of the product experiences and business processes that make up your area of focus.- Develop a deep familiarity with the source data and its generating systems through documentation, interacting with the engineering teams, and systematic data profiling.- Translate the business questions into causal questions and finding innovative ways to answer them.- Design and develop robust solutions to infer causal relationships from observational data.- Build causal ML models to identify and quantify the impact of different growth drivers.- Design and run A/B test experiments to answer causal questions if needed.- Deliver well-formed, relevant, reliable, and actionable insights and recommendations to support data-driven decision making through deep analysis.- Contribute to Talabat’s data culture: drive adoption of causal inference through talks, workshops, and processes allowing good collaboration.

Requirements - Technical Experience- Experienced in Python/R/SQL.- Strong command over the entire data analysis lifecycle, and experience with different types of analysis including: descriptive, exploratory, causal, and predictive analysis.- Strong understanding and hands-on experience in probability and statistics, data analysis, AB testing, ML algorithms and causal inference.- Good understanding and practical experience with tools such as causal DAGs, regression, propensity matching, event studies, regression discontinuity, and counterfactual scenario simulation.

**Qualifications**:
- A Bachelor or a Master degree in quantitative field with 3+ years of overall experience working in data science or machine learning.- Curiosity and willingness to dive into our data and make sense out of it. On top of that, you are able to build causation theories from correlations in data and provide solid proofs for these theories.- Loves the WHY questions and highly interested in deep understanding of customer behavior and the drivers of retention.- A team player who is comfortable with collaboration as well as working independently with minimum guidance.- Have good communication skills and can explain technical concepts clearly for technical and non-technical team members.- A strong sense of ownership and accountability.- A ‘keep it simple’ approach to #makeithappen.- Able to work in a fast-paced environment.

Nice to Have- A Phd in Causal Machine Learning or equivalent.- Experience with data science in an online consumer product setting.- Experience in writing high quality software.- Experience in building and deploying machine learning systems.- Familiarity with BigQuery and the Google Cloud Platform.- Data engineering and data pipeline development experience (e.g. via Airflow).