About the opportunity
As the leading delivery company in the region, talabat has 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.
As a data scientist on the algorithms track, your mission will be to improve the quality of the decisions made across product and business via relevant, reliable, and actionable data. You will own a particular domain across product and business and will work closely with the corresponding product and business managers as part of a talented team of data scientists and data engineers. You will own the entire data value chain including logging, data modeling, analysis, reporting, and experimentation.
Responsibilities
- Leveraging ambiguous business problems as opportunities to drive objective criteria using data.
- Solving complex business problems using the simplest most appropriate Algorithms to deliver business value.
- Designing and implementing effective and impactful machine learning systems in production.
- Developing a deep understanding of the product experiences and business processes that make up your area of focus.
- Developing a deep familiarity with the source data and its generating systems through documentation, interacting with the engineering teams, and systematic data profiling.
- Contributing heavily to the design and maintenance of the data models that allow us to measure performance and comprehend performance drivers for your area of focus.
- Working closely with product and business teams to identify important questions that can be answered effectively with data.
- Delivering well-formed, relevant, reliable, and actionable insights and recommendations to support data-driven decision-making through deep analysis and automated reports.
- Designing, planning, and analyzing experiments (A/B and multivariate tests).
- Supporting product and business managers with KPI design and goal setting.
- Mentoring other data scientists in their growth journeys.
- Contributing to improving our ways of work, our tooling, and our internal training programs.
What you need to be successful
Technical Experience
- Experience in machine learning, deep learning, recommendation systems, pattern recognition, data mining and artificial intelligence.
- Deep knowledge and experience in ML algorithms and frameworks (e.g. Scikit-learn, XGBoost, LightGBM, CatBoost, SVMs, Keras, TensorFlow, PyTorch ...).
- Excellent SQL.
- Competence with reproducible data analysis using Python or R.
- Familiarity with data modeling and dimensional design.
- Strong command over the entire data lifecycle including; problem formulation, data auditing, rigorous analysis, interpretation, recommendations, and presentation.
- Familiarity with different types of analysis including; descriptive, exploratory, inferential, causal, and predictive analysis.
- Deep understanding of the various experiment design and analysis workflows and the corresponding statistical techniques.
- Familiarity with product data (impressions, events, ..) and product health measurement (conversion, engagement, retention, ..).
- Familiarity with BigQuery and the Google Cloud Platform is a plus.
- Data engineering and data pipeline development experience (e.g. via Airflow) is a plus.
Qualifications
- Bachelor's degree in engineering, computer science, technology, or similar fields. A postgraduate degree is a plus but not required.
- 3-5 years of overall experience working in data science or machine learning.
- Experience doing data science in an online consumer product setting is a plus.
- A good problem solver with a ‘figure it out’ growth mindset.
- An excellent collaborator.
- An excellent communicator.
- A strong sense of ownership and accountability.
- A ‘keep it simple’ approach to #makeithappen.