Data Scientist

About This Role

Who we are

GIG Gulf is part of the Gulf Insurance Group (GIG), the #1 largest regional composite insurer in the Middle East and North Africa, with presence in 13 markets including the United Arab Emirates, Bahrain, Oman, Qatar, Saudi Arabia, Algeria, Egypt, Iraq, Jordan, Kuwait, Lebanon, and Turkey. GIG Group reported consolidated assets of US$ 3.83 billion and $69 million net profit for the year 2023. The majority shareholder of GIG Group is Toronto based Fairfax Financial Holding, a global leader in insurance and reinsurance with a presence in 40 markets.

GIG Gulf is an ‘A’ rated regional insurer with a top 5 position in each of its markets (UAE, Oman, Qatar, Bahrain). GIG Gulf has been present in the region for over 70 years with a strategic focus on both growth and investments and is a one stop shop offering a wide range of insurance products and services that cater to a broad variety of needs for corporates, SMEs and individual customers throughout UAE, Oman, Bahrain, and Qatar. GIG Gulf also owns a 50% stake in GIG Saudi. Our strategic objectives and guiding principles are focused on Regional Growth, Customer Experience and Digital Transformation.

GIG Gulf has created a diverse and inclusive working environment and culture with a workforce of over 800 employees, with over 60 nationalities, across 15 branches and retail shops region-wide and over 1 million customers. GIG Gulf is a caring partner that encourages customers to achieve their goals and live an inspiring and fulfilling life. We are obsessed with customer feedback and continuously evolving to become the region’s digital insurer of reference, committed to running our operations in a responsible, sustainable way.

Job purpose:

The role will be versatile, covering different Business Lines and parts of the insurance value chain. The main mission will be to deploy strong technical expertise in both enhancing the data strategy and delivering solutions to business challenges.

The ideal candidate will be successful in this role not only through building on and developing subject matter expertise in Data Science techniques, but also through working closely and collaboratively with the business. This will be key to understand how to best identify domains where Data Science can help improve performance – and to develop solutions that are practical and adopted by the relevant business stakeholders.

Key Responsibilities:

Technical Development & Data Solutions:

• Conduct exploratory data analysis on large, structured and unstructured datasets to identify patterns, correlations, and opportunities for performance improvement.
• Develop, validate, and refine mathematical and statistical models aligned with business needs.
• Apply data science techniques to real-world use cases (e.g., customer segmentation, pricing optimization, network optimization, etc.).
• Design and implement innovative data science and Generative AI solutions (e.g., RAG pipelines, LLM-based applications, agentic workflows) to address business challenges.
• Contribute to the continuous improvement and innovation of data science methodologies, tools, and approaches within the organization.

Problem Framing & Solution Design:

• Develop a deep understanding of business needs through close collaboration with stakeholders across functions (sales, product, etc.).
• Translate business challenges and strategic objectives into well-defined data science problems.
• Identify, evaluate, and select appropriate analytical methodologies, machine learning techniques, and solution architectures.
• Engage stakeholders to assess feasibility, relevance, and expected impact of proposed solutions.
• Ensure alignment and buy-in at key stages of data science initiatives.

Communication, Adoption & Business Impact:

• Present and communicate analytical findings, models, and recommendations to business stakeholders and senior management.
• Translate complex technical outputs into clear, actionable insights for non-technical audiences.
• Promote adoption of data-driven solutions across the organization.
• Develop documentation, training materials, and deliver knowledge-sharing sessions (e.g., demos).
• Contribute to fostering a data-driven culture and continuous innovation within the organization.

Operational & Technical Responsibilities:

• Design, develop, and maintain end-to-end data science solutions, from data ingestion and processing to model deployment and monitoring.
• Build and optimize data pipelines leveraging cloud technologies (AWS: SageMaker, Glue, Athena, Lambda, Bedrock , … ; Azure: AI foundry, …) to support scalable analytics and machine learning use cases.
• Develop, train, and validate machine learning and statistical mode ls to solve business problems and improve decision-making.
• Implement and maintain MLOps best practices , including CI/CD pipelines, model versioning, performance monitoring, and automated retraining.
• Apply advanced analytics techniques (e.g., data mining, text analytics, network analysis) on large and complex datasets.
• Design and implement Generative AI solutions (e.g., RAG pipelines, LLM-based applications, agentic workflows) where relevant to business use cases.
• Ensure data quality, robustness, and reproducibility of models and analytical workflows.
• Develop and maintain data visualizations, dashboards, and reporting solutions (e.g., Power BI) to support business insights and transparency.
• Collaborate with data engineers and IT teams to ensure proper data architecture, data availability, and system integration.
• Document methodologies, models, and technical solutions to ensure maintainability and knowledge sharing.

Essential Requirements:

• Master’s degree in quantitative field (e.g., Data Science, Engineering, Actuarial Science, Mathematics, Statistics).
• Solid understanding of the scientific method and ability to translate business problems into mathematical/statistical models.
• Strong proficiency in Python .
• Hands-on experience with classical machine learning and statistical techniques (e.g., scikit-learn, TensorFlow, Statsmodels).
• Knowledge of Generative AI techniques (e.g., RAG, LLM fine-tuning, agentic workflows).
• Proven experience in data processing and analysis (e.g., data mining, large datasets, text analytics).
• Strong knowledge of SQL and experience working with relational databases.
• Experience with AWS data ecosystem (e.g., SageMaker, Athena, Glue, Lambda, Step Function).
• Experience deploying machine learning models into production , including MLOps practices (CI/CD, monitoring, model lifecycle management).
• Ability to communicate insights effectively to both technical and non-technical stakeholders.