Lead Data Scientist

Jex Recruitment | Connecting top talent with leading companies; Abu Dhabi; On-site; AED 280,000 - 520,000; Full time

Job summary

Jex Recruitment is seeking an exceptional Data Scientist in Abu Dhabi to transform large datasets into insights, models and intelligent systems for AI products and research initiatives.

You will collaborate with AI Researchers, ML Engineers and Software Engineers to develop novel approaches, evaluate model performance and deploy production-ready solutions in a research-led environment.

Qualifications

• Bachelor's, Master's or PhD in Computer Science, Data Science, Mathematics, Statistics, Artificial Intelligence or a related discipline.
• 5+ years of commercial experience in Data Science or Machine Learning.
• Strong mathematical foundation including probability, statistics and optimisation.
• Experience with SQL and large-scale data processing.
• Strong experience with Scikit-learn, Pandas, NumPy and modern ML frameworks.
• Experience working with PyTorch or TensorFlow.
• Knowledge of feature engineering, model selection and hyperparameter optimisation.
• Experience deploying models into production environments.
• Strong understanding of evaluation metrics, experimentation and statistical significance.
• Excellent communication and problem-solving skills.
• Experience working with Large Language Models (GPT, Llama, Claude, Mistral or similar).
• Knowledge of Retrieval-Augmented Generation (RAG).
• Experience with reinforcement learning or agentic AI systems.
• Familiarity with vector databases and embedding models.
• Experience with distributed computing (Spark, Ray or Dask).
• Knowledge of MLOps tools including MLflow, Kubeflow or SageMaker.
• Experience using Kubernetes and Docker.
• Exposure to computer vision, speech or multimodal AI.
• Publications, patents or open-source contributions within AI or machine learning.

Responsibilities

• Design, build and deploy advanced statistical and machine learning models.
• Analyse large-scale structured and unstructured datasets to uncover meaningful insights.
• Develop predictive models using classical ML techniques and modern deep learning approaches.
• Partner with AI Researchers to evaluate foundation models, LLMs and multimodal systems.
• Build data pipelines, feature engineering frameworks and model evaluation methodologies.
• Design experiments and A/B testing frameworks to validate model improvements.
• Evaluate new research papers and translate academic advancements into production-ready solutions.
• Work with cloud-native data platforms and distributed computing environments.
• Present technical findings to both technical and executive stakeholders.

Skills

Statistical modeling

ML algorithm design

SQL and data processing

Scikit-learn, Pandas, NumPy

PyTorch or TensorFlow

Feature engineering

Model deployment

Experimentation & evaluation

ML research & publications

Reinforcement learning / agentic AI

RAG / retrieval augmented generation

Vector databases & embeddings

Distributed computing (Spark, Ray, D3)

MLOps tools (MLflow, Kubeflow, SageMak

Kubernetes & Docker

Computer vision / multimodal AI

Communication skills

Problem solving

Education

Bachelor's/Master's/PhD in CS/DS/Math/Statistics/AI

Tools

SQL

MLflow

Kubeflow

SageMaker

Kubernetes

Docker

Spark

Ray

Dask

Job description

We are building the next generation of artificial intelligence systems, bringing together researchers, engineers and product teams to solve complex real-world problems using state-of-the-art machine learning. Our work spans large language models (LLMs), multimodal AI, agentic systems, reinforcement learning, computer vision and advanced predictive modelling.

This is an opportunity to work in a research-led environment where experimentation, innovation and scientific thinking are valued as highly as engineering excellence.

The Role

We are looking for an exceptional Data Scientist to transform large, complex datasets into insights, models and algorithms that power cutting-edge AI products and research initiatives.

You’ll work alongside AI Researchers, Machine Learning Engineers and Software Engineers to develop novel approaches, evaluate model performance and build intelligent systems that solve meaningful business and scientific challenges.

This role suits someone who enjoys working at the intersection of statistics, machine learning and applied AI.

Responsibilities

• Design, build and deploy advanced statistical and machine learning models.


• Analyse large-scale structured and unstructured datasets to uncover meaningful insights.


• Develop predictive models using classical ML techniques and modern deep learning approaches.


• Partner with AI Researchers to evaluate foundation models, LLMs and multimodal systems.


• Build data pipelines, feature engineering frameworks and model evaluation methodologies.


• Design experiments and A/B testing frameworks to validate model improvements.


• Evaluate new research papers and translate academic advancements into production-ready solutions.


• Work with cloud-native data platforms and distributed computing environments.


• Present technical findings to both technical and executive stakeholders.

Required Experience

• Bachelor's, Master's or PhD in Computer Science, Data Science, Mathematics, Statistics, Artificial Intelligence or a related discipline.


• 5+ years of commercial experience in Data Science or Machine Learning.


• Strong mathematical foundation including probability, statistics and optimisation.


• Experience with SQL and large-scale data processing.


• Strong experience with Scikit-learn, Pandas, NumPy and modern ML frameworks.


• Experience working with PyTorch or TensorFlow.


• Knowledge of feature engineering, model selection and hyperparameter optimisation.


• Experience deploying models into production environments.


• Strong understanding of evaluation metrics, experimentation and statistical significance.


• Excellent communication and problem-solving skills.


• Experience working with Large Language Models (GPT, Llama, Claude, Mistral or similar).


• Knowledge of Retrieval-Augmented Generation (RAG).


• Experience with reinforcement learning or agentic AI systems.


• Familiarity with vector databases and embedding models.


• Experience with distributed computing (Spark, Ray or Dask).


• Knowledge of MLOps tools including MLflow, Kubeflow or SageMaker.


• Experience using Kubernetes and Docker.


• Exposure to computer vision, speech or multimodal AI.


• Publications, patents or open-source contributions within AI or machine learning.