Lead Data Scientist

Role & responsibilities:

• Lead the design, development, deployment, and optimization of machine learning and AI solutions that solve high-impact business problems.
• Own the end-to-end data science lifecycle, from problem formulation and data exploration to model development, deployment, monitoring, and continuous improvement.
• Lead and mentor a team of Data Scientists, ML Engineers, and Analysts, establishing best practices for experimentation, modeling, documentation, and productionization.
• Develop advanced models for predictive analytics, forecasting, classification, recommendation, optimization, and anomaly detection.
• Drive Generative AI and LLM initiatives, including RAG systems, embeddings, fine-tuning, prompt engineering, evaluation, and AI-agent applications where relevant.
• Work closely with Product, Engineering, Business, and senior leadership teams to translate business requirements into data-driven solutions.
• Establish ML architecture and MLOps practices covering model deployment, CI/CD, experiment tracking, monitoring, governance, and model performance.
• Work with large and complex datasets using Python, SQL, Spark/Databricks, and cloud-based data platforms.
• Define appropriate KPIs and measure the business impact and ROI of data science initiatives.
• Present technical findings, model results, risks, and recommendations to senior stakeholders and executive leadership.
• Ensure models meet appropriate standards for accuracy, scalability, explainability, security, privacy, and responsible AI.
• Stay current with developments in AI, machine learning, GenAI, and emerging data technologies, and identify opportunities to apply them within the organization.

Preferred candidate profile:

• 5 to 10 years of professional experience in Data Science, Machine Learning, AI, or a closely related field.
• Proven experience leading end-to-end machine learning projects and taking models into production.
• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, AI/ML, or another quantitative discipline.
• Strong proficiency in Python and SQL.
• Strong understanding of statistics, machine learning, feature engineering, model evaluation, and experimentation.
• Hands-on experience with Scikit-learn and at least one deep-learning framework such as PyTorch or TensorFlow.
• Practical experience with Generative AI, LLMs, RAG, embeddings, vector databases, and/or AI agents is highly desirable.
• Experience with at least one major cloud platform: AWS, Azure, or Google Cloud.
• Strong knowledge of MLOps, Docker, CI/CD, model deployment, monitoring, and production ML systems.
• Experience with Spark, Databricks, data warehouses, or modern data platforms is an advantage.
• Demonstrated experience leading, mentoring, or managing data science teams.
• Strong communication skills with the ability to explain complex technical concepts to non-technical and executive stakeholders.
• Strong business acumen and ability to ML algorithmit's being able connect technical solutions to revenue growth, cost reduction, operational efficiency, customer experience, or other measurable outcomes.
• Experience working in remote, multicultural, and cross-functional environments is preferred.
• UAE/GCC market experience is an advantage, but not necessarily a requirement for remote positions.
• Strong problem-solving, analytical, strategic thinking, and decision-making abilities.