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About the Role

As Manager, Data Scientist - AI within the Applied AI Tribe, you will lead a team of data scientists building and shipping ML and generative AI systems that power decisions across talabat's product and business. You are equally a people leader and a technical practitioner, close enough to the work to make great technical decisions and grow your team, while owning the roadmap and delivery. Your team's work spans the full AI lifecycle: from framing ambiguous business problems through data modelling, feature engineering, model training, deployment, and production monitoring. A significant focus will be leveraging LLMs, agentic systems, and generative AI to automate decisions, enrich data, and build intelligent experiences at scale, all measured against Net AI Value Delivered.

Responsibilities

Lead, grow, and retain a team of data scientists, setting a high bar for technical quality, fostering a culture of ownership, and supporting each person's career development.
Partner with recruiting to attract top data science and ML talent as the tribe scales.
Mentor team members in ML best practices, agentic system design, production engineering, and stakeholder communication.
Run effective team rituals, planning, design reviews, retrospectives, that keep the team aligned and moving with pace.
Translate ambiguous business problems into well-scoped ML and AI solutions with clear, measurable success criteria tied to the tribe's north star.
Own the team's technical roadmap: prioritise high-impact work across data enrichment, business automation, self-serve tooling, and agentic product features.
Champion harness-first thinking, ensure evaluation pipelines, LLM observability, tooling, and infrastructure are in place before agent logic is built on top.
Embed evaluation into every product iteration: golden datasets, stakeholder-aligned metrics, and weekly eval jobs that guide what gets built, fixed, or retired.
Oversee the full ML lifecycle: data pipelines, feature engineering, model training, production deployment, serving, and monitoring.
Drive adoption of LLMs and generative AI for data enrichment, smart content understanding, and automated decision-making at scale.
Design and analyse experiments (A/B and multivariate) to rigorously measure model and product impact.
Elevate ML and engineering standards across the team, improving MLOps, code quality, tooling, and internal learning programmes.
Partner with product managers and business teams to identify high-value AI opportunities and shape them into the team's roadmap.
Communicate clearly with senior stakeholders, from problem framing through to results and recommendations.
Collaborate with engineering teams to understand data systems, build reliable data models, and ensure smooth production integration.

Requirements

Deep expertise in machine learning, generative AI, deep learning, NLP, recommendation systems, and data mining.
Hands-on knowledge of ML and GenAI frameworks: Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, and LLM fine-tuning.
Proficiency with the OpenAI SDK and familiarity with major LLM provider APIs for building and orchestrating production AI systems.
Hands-on experience with LangGraph for building stateful, multi-step agentic workflows and complex agent orchestration patterns.
Experience with Hugging Face (Transformers, Hub, Inference API) for model sourcing, fine-tuning, and deployment.
Solid understanding of embeddings, dense and sparse retrieval, vector databases, and semantic search, for building RAG pipelines and similarity-based applications.
Strong software engineering fundamentals: clean production code, data structures and algorithms, ML system design.
Proven experience shipping and monitoring ML models in production, with a solid grasp of MLOps practices.
Strong data and ML engineering skills: building and orchestrating data pipelines (e.g., Airflow) and robust feature engineering.
Excellent SQL and Python; solid statistical foundations including experiment design, causal inference, and predictive methods.
Familiarity with agentic system design (harness-first thinking, LLM observability, evaluation pipelines, and production feedback loops) is a strong plus.
Experience with BigQuery and Google Cloud Platform is a plus.
Bachelor's degree in Engineering, Computer Science, or a related field.
6+ years of experience in data science, ML engineering, and/or generative AI, including shipping models to production.
2+ years managing or leading a data science or ML team, with a track record of developing talent and delivering through others.
Experience building ML systems in an online consumer product environment is a strong plus.
Curiosity over certainty: you're excited to try the new model or framework this week.
Ownership: you treat cost, latency, and business impact as your problem, and your team's.
Bias to build: a prototype in a real workflow beats a perfect design doc every time.
Comfortable with ambiguity: most of what we build has no playbook yet, and you help write it.
Keep it simple: pick the approach that delivers the most value with the least complexity, and #makeithappen.

Skills

Machine Learning
Generative AI
Deep Learning
NLP
Recommendation Systems
Data Mining
Scikit-learn
XGBoost
LightGBM
PyTorch
TensorFlow
Transformers
LLM fine-tuning
OpenAI SDK
LangGraph
Hugging Face
Embeddings
Vector Databases
Semantic Search
RAG pipelines
Software Engineering
MLOps
Data Pipelines
Feature Engineering
SQL
Python
Statistical Foundations
Experiment Design
Causal Inference
Predictive Methods
Agentic System Design
BigQuery
Google Cloud Platform

Location

Kuwait

Work Type

Full-time

Experience Level

6+ years of experience in data science, ML engineering, and/or generative AI
2+ years managing or leading a data science or ML team

Education Level

Bachelor's degree in Engineering, Computer Science, or a related field

About the Company

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.