Director Data Science

Responsibilities:

• Lead, mentor, and grow a high-performing team of data scientists, ML engineers, and analysts.
• Define the data science vision, roadmap, and strategy aligned with business objectives.
• Partner closely with Product, Engineering, Marketing, and Business leaders to identify high-value opportunities for AI/ML.
• Promote data-driven decision-making across the organisation.
• Provide hands-on guidance in designing, developing, and deploying advanced ML and AI models.
• Oversee end-to-end lifecycle of data science projects, problem scoping, data exploration, modelling, evaluation, deployment, and monitoring.
• Personalisation and Recommendation Systems (e. g., ranking models, user intent modelling, collaborative and content-based filtering).
• Search Algorithms and Retrieval Optimisation (semantic search, vector search, query expansion).
• Generative AI (LLMs, prompt engineering, RAG pipelines, content generation, automation).
• Ensure scalable, high-quality production ML systems in collaboration with engineering.
• Prioritise and manage multiple complex projects with cross-functional teams.
• Translate business needs into technical requirements and actionable insights.
• Communicate technical concepts to non-technical stakeholders effectively.
• Establish best practices for experimentation, model governance, MLOps, monitoring, and continuous improvement.
• Develop frameworks for model explainability, fairness, and compliance.
• Optimise processes to improve team efficiency and output quality.

Requirements:

• 11-15 years of experience in data science, machine learning, or AI roles, with at least 4-6 years in leadership or management.
• Proven track record of building and deploying ML models at scale.
• Personalisation and Recommendation Systems.
• Search, Information Retrieval and Ranking.
• Generative AI (LLMs, Transformers, RAG, fine-tuning).
• Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience with distributed data processing (Spark, Hadoop) and cloud platforms (AWS/Azure/GCP).
• Hands-on experience with MLOps tools: MLflow, Kubeflow, Airflow, etc.
• Exceptional problem-solving, communication, and leadership skills.
• Ability to thrive in fast-paced, ambiguous environments.
• Master's in Computer Science, Data Science, Machine Learning, Statistics, or a related field.
• Experience working in product-led tech companies.
• Experience managing data science teams.
• Familiarity with real-time ML systems and large-scale experimentation platforms.