Job description

Company Description

Since launching in Kuwait in 2004, talabat, the leading on-demand food and Q-commerce app for everyday deliveries, has been offering convenience and reliability to its customers. talabat’s local roots run deep, offering a real understanding of the needs of the communities we serve in eight countries across the region.
 

We harness innovative technology and knowledge to simplify everyday life for our customers, optimize operations for our restaurants and local shops, and provide our riders with reliable earning opportunities daily.
 

Here at talabat, we are building a high performance culture through engaged workforce and growing talent density. We're all about keeping it real and making a difference. Our 6,000+ strong talabaty are on an awesome mission to spread positive vibes. We are proud to be a multi great place to work award winner.

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.

As a Sr. Data Scientist (AI & ML) on the global AI hub, your mission will be to design, build, and ship the machine learning and generative AI systems that power decisions across product and business. You will own a particular domain end to end, working closely with product and business managers as part of a talented team of data scientists and machine learning engineers. You will own the full ML lifecycle,  from problem framing, data modeling, and feature engineering through model training, deployment, serving, and monitoring in production. Many of our initiatives will focus on leveraging Generative AI and LLMs for tasks such as data enrichment, smart content understanding, and automated decision-making to enhance user experiences and business operations at scale.

Responsibilities

• Framing ambiguous business problems as well-defined machine learning and data science problems, with clear, objective success criteria.

  
  

• Providing high-quality, impactful insights and data-driven recommendations through rigorous analysis and automated reporting to drive strategic organizational choices.

  
  

• Designing, building, and shipping end-to-end machine learning and generative AI systems in production — spanning data pipelines, feature engineering, model training, serving, and monitoring.

  
  

• Taking on engineering-heavy work end to end: architecting robust ML-based systems, writing clean and scalable production code, and training, deploying, and maintaining reliable ML models that solve real business problems at scale.

  
  

• Training, evaluating, and iterating on models — selecting the simplest, most appropriate algorithms and architectures to deliver measurable business value.

  
  

• Leveraging LLMs and generative AI for data enrichment, smart content understanding, and automated decision-making within production systems.

  
  

• Building and maintaining the data models, features, and pipelines that power model training and allow us to measure performance and its drivers for your area of focus.

  
  

• Designing, planning, and analyzing experiments (A/B and multivariate tests) to rigorously measure model and product impact.

  
  

• Developing deep familiarity with source data and its generating systems through documentation, collaboration with engineering teams, and systematic data profiling.

  
  

• Partnering with product and business teams to identify high-impact opportunities and translate them into ML solutions and actionable, data-driven recommendations.

  
  

• Mentoring other data scientists in their growth journeys.

  
  

• Elevating engineering and ML best practices — improving our ways of working, tooling, MLOps, and internal training programs.

  
  

Technical Experience

• Deep expertise in machine learning, generative AI, deep learning, recommendation systems, NLP, pattern recognition, data mining.

  
  

• Deep hands-on knowledge of ML and GenAI frameworks (e.g. Scikit-learn, XGBoost, LightGBM, CatBoost, SVMs, Keras, TensorFlow, PyTorch, Transformers, LLM fine-tuning).

  
  

• Strong software engineering fundamentals: excellent coding skills, a solid grasp of data structures and algorithms, and proven ability in both general system design and ML system design.

  
  

• Proven experience building, deploying, serving, and monitoring ML models in production, with a strong grasp of MLOps practices.

  
  

• Strong data and ML engineering skills, including building and orchestrating data and training pipelines (e.g. via Airflow) and robust feature engineering.

  
  

• Excellent SQL and competence with reproducible analysis and modeling in Python.

  
  

• Solid statistical foundations, including experiment design and analysis (A/B and multivariate) and inferential, causal, and predictive methods.

  
  

• Familiarity with data modeling and dimensional design.

  
  

• Strong command over the entire ML lifecycle, from problem formulation and data auditing through modeling, deployment, interpretation, and presentation.

  
  

• Familiarity with product data (impressions, events, etc.) and product health measurement (conversion, engagement, retention, etc.).

  
  

• Experience with LLMs and NLP-based solutions for data enrichment and smart automation is a plus.

  
  

• Familiarity with BigQuery and the Google Cloud Platform is a plus.

  
  

Qualifications

• Bachelor's degree in engineering, computer science, technology, or similar fields. A postgraduate degree is a plus but not required.

  
  

• 5+ years of experience across data science, machine learning engineering, and generative AI, including shipping ML models to production.

  
  

• Experience building ML systems 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.