About us:

Swvl is a revolutionary idea that was born from passion, loyalty, and persistence to face all challenges that come our way. It started with an observation turning into a realization; too many cars on the streets, wasting our limited resources: time, space, and money.

In 4 years Swvl became the first 1.5 billion unicorn in the Middle East to list on NASDAQ and currently the 2nd best-funded startup in the MENA region. With a presence and operations in up to 10 countries worldwide and a vision to be active on 6 continents.

Our main goal is not just to facilitate commuting, but a hunger to strive for solutions, encourage the contribution of youth in innovation, and inspire change.

We are looking for an engaged and enthusiastic Engineer to join our team of talented engineers that share a common interest in distributed systems, their scalability and continued development.

About the role:

A senior staff data scientist is considered a subject matter expert with a very good grasp of the business and technical domains. Orchestrates large-scale technical projects across engineering and other external departments. A track record of end-to-end leadership of successful medium projects spanning several teams that impacts business or engineering KPIs. A great mentor who is able to push others in the right direction for their growth. Doesn't stop at blockers in execution and is able to unblock projects. Drives impactful changes against stability, quality, architecture and design. Takes initiative rather than wait for external motivation.

About Marketplace Tribe:

Marketplace is the platform that connects customers and captains. Our goal is to be reliable, profitable, and scalable. We do this by focusing on ETAs (Estimated Time of Arrivals), pricing, dispatching, supporting multiple choices of rides, and the network’s efficient coverage growth.

Responsibilities & Duties:

  • Problem Discovery
    • Works with business & product stakeholders to define data science project scope and requirements, and develops execution plans to achieve the project success criteria
    • Translates business requirements into technical specifications (e.g. mathematical models)
  • Solution Design
    • Builds, deploys and monitors predictive and machine learning models that leverage SWVL data to improve our customers and captains experience and solve SWVL core AI problems
    • Analyzes different approaches to solve optimization problems and uses their technical expertise to decide which approach given accuracy and performance trade-offs
  • Execution
    • Leads and ships large-scale data projects of high complexity from conceptualization to market release
    • Collaborates with engineers to transform POC to production level while ensuring models scalability, observability, robustness, and accuracy.
    • Works cross functionally with business, product and engineering teams
  • Leadership
    • Leads teams of data scientists and engineers to deliver data projects
    • Mentors senior and staff data scientists given their breadth and depth of knowledge in various data science subfields
    • Oversees technical quality to ensure that it adheres to Swvl’s expectations for excellence without compromising the continuous delivery of business value
  • Vision & Strategy
    • Envisions and plans short-term and long-term marketplace roadmaps as transportation domain experts
    • Identifies technology gaps related to Swvl and come up with initiatives to build proprietary SOTA systems

    Qualifications:

    • Experience in transportation domain is a huge plus
    • Master's degree in CS, CE, ML or 10+ years of industrial experience in machine learning
    • Solid understanding of industrial mathematics and statistics
    • Solid understanding of best practices in feature extraction, dimensionality reduction, model validation, and classification
    • Hands-on experience with SQL, Docker, Git, AWS EC2, S3, ML/DL frameworks such as Tensorflow, Keras, Pytorch, XGBoost, Scikit-learn, Statsmodels, PyMC3
    • Proven track record of managing successful production machine learning systems
    • Experience with machine learning lifecycle platforms (i.e. mlflow)
    • Knowledge of distributed storage and processing of big data (Hadoop, Spark, etc.)
    • Knowledge of operations research (OR) is a plus
    • Strong problem-solving and analytical skills
    • Experience in reading research papers, publishing and doing literature reviews
    • Creative and outside the box thinking
    • Excellent communication and business acumen skills
    • Sufficient coding and design concepts. Knowledge of programming languages as python