Staff Data Scientist
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Key skills for this role
About the Role
talabat is seeking a Staff Data Scientist to join its global AI hub, designing and shipping machine learning and generative AI systems that power decisions across product and business.
Key Skills for This Role
Responsibilities
- Frame ambiguous business problems as well defined machine learning and data science problems with clear success criteria
- Provide high quality, impactful insights and data driven recommendations through rigorous analysis and automated reporting
- Design, build, and ship end to end machine learning and generative AI systems in production, spanning data pipelines, feature engineering, model training, serving, and monitoring
- Take on engineering heavy work end to end: architect robust ML based systems, write clean and scalable production code, and train, deploy, and maintain reliable ML models
- Train, evaluate, and iterate on models, selecting the simplest, most appropriate algorithms and architectures to deliver measurable business value
- Leverage LLMs and generative AI for data enrichment, smart content understanding, and automated decision making within production systems
- Build and maintain data models, features, and pipelines that power model training and performance measurement
- Design, plan, and analyze experiments (A/B and multivariate tests) to rigorously measure model and product impact
- Develop deep familiarity with source data and its generating systems through documentation, collaboration, and systematic data profiling
- Partner with product and business teams to identify high impact opportunities and translate them into ML solutions and actionable recommendations
- Mentor other data scientists in their growth journeys
- Elevate engineering and ML best practices, improving ways of working, tooling, MLOps, and internal training programs
Requirements
- 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, solid grasp of data structures and algorithms, proven ability in 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 testing)
Full Job Posting
About talabat
- talabat is the leading on demand food and Q commerce app for everyday deliveries, operating in eight countries across the region.
- The company harnesses innovative technology to simplify everyday life for customers, optimize operations for restaurants and shops, and provide riders with earning opportunities.
Job Description
- As a Staff Data Scientist (AI & ML) on the global AI hub, you will design, build, and ship 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 to production monitoring, with initiatives focusing on Generative AI and LLMs for data enrichment, smart content understanding, and automated decision making.
Responsibilities
- Framing ambiguous business problems as well defined machine learning and data science problems with clear success criteria.
- Providing high quality, impactful insights and data driven recommendations through rigorous analysis and automated reporting.
- Designing, building, and shipping end to end machine learning and generative AI systems in production.
- Taking on engineering heavy work end to end: architecting robust ML based systems, writing clean production code, and deploying reliable models.
- Training, evaluating, and iterating on models to deliver measurable business value.
- Leveraging LLMs and generative AI for data enrichment, smart content understanding, and automated decision making.
- Building and maintaining data models, features, and pipelines for model training and performance measurement.
- Designing, planning, and analyzing experiments (A/B and multivariate tests) to measure model and product impact.
- Developing deep familiarity with source data and its generating systems.
- Partnering with product and business teams to identify high impact opportunities.
- Mentoring other data scientists.
- Elevating engineering and ML best practices.
Qualifications
- 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, solid grasp of data structures and algorithms, and proven ability in system design and ML system design.
- Proven experience building, deploying, serving, and monitoring ML models in production with strong 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 testing).
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