Associate Director, Data Science

Job Summary

About the Role: SP is a leader in risk management solutions leveraging automation and AI/ML. This role is a unique opportunity for hands-on ML scientists and NLP/Gen AI/ LLM scientists to grow into the next step in their career journey and apply her or his technical expertise in ML, deep learning, GenAI, and LLMs to drive business value for multiple stakeholders while conducting cutting-edge applied research around LLMs, Gen AI, and related areas.

Grade Level (for internal use): 12

The Team: SP is a leader in risk management solutions leveraging automation and AI/ML. This role is a unique opportunity for hands-on ML scientists and NLP/Gen AI/ LLM scientists to grow into the next step in their career journey and apply her or his technical expertise in ML, deep learning, GenAI, and LLMs to drive business value for multiple stakeholders while conducting cutting-edge applied research around LLMs, Gen AI, and related areas.

Responsibilities and Impact:

• Design, develop, and deploy advanced AI/ML solutions using Machine Learning, Generative AI, NLP, LLMs, and RAG frameworks, covering the full model lifecycle from data ingestion and preprocessing to fine-tuning, prompt engineering, and production deployment.
• Partner with cross-functional teams including Product, Business, Data Science, MLOps, Machine Learning Engineering, and Software Engineering to deliver scalable AI solutions that address complex business challenges and drive measurable impact.
• Establish and maintain high-quality documentation of model development processes, methodologies, and implementation details to support governance, knowledge sharing, and operational excellence.
• Champion AI/ML best practices and governance standards, ensuring models are built with scalability, reliability, compliance, and performance in mind while adhering to organisational development frameworks and industry-leading practices.

What Were Looking For:

• Lead the design, development, and deployment of advanced AI/ML solutions, leveraging Machine Learning, NLP, RAG, and Generative AI technologies to solve complex business problems and deliver measurable business impact.
• Transform large volumes of structured and unstructured data into actionable insights, applying statistical analysis, predictive modelling, and data-driven decision-making frameworks that influence strategic and tactical initiatives.
• Drive innovation in AI and Generative AI applications, including the development and optimisation of LLM-powered solutions, retrieval systems, and intelligent automation capabilities within enterprise environments.
• Build and productionise scalable machine learning models, ensuring robust performance, reliability, and maintainability through best practices in model development, testing, and deployment.
• Collaborate with senior stakeholders, data scientists, engineers, and business teams to translate business challenges into analytical solutions and effectively communicate insights through compelling data visualisations and recommendations.
• Shape the future AI strategy of the organisation, with the opportunity to work on cutting-edge technologies, influence technical direction, mentor teams, and solve high-impact problems at enterprise scale in a fast-evolving AI landscape.

Key Qualifications

• 12+ years of experience and strong expertise in Machine Learning and Generative AI, including feature engineering, A/B testing, experimental design, prompt engineering, LLM fine-tuning, and RAG pipeline development.
• Proficiency in Python programming with the ability to develop high-quality, production-ready code and integrate LLMs or multimodal models into production systems.
• Hands-on experience with AI/ML frameworks and tools, including LangChain, LangGraph, PyTorch, TensorFlow, Agentic AI, MCP concepts, Docker, Kubernetes, and MLOps practices.
• Experience working with cloud and DevOps technologies, particularly AWS, Git-based workflows, GitHub Actions, CI/CD pipelines, and version control.

Key Soft Skills

• Analytical and problem-solving skills to design experiments, optimise model performance, and solve complex AI/ML challenges.
• Innovation mindset and curiosity to stay current with emerging AI technologies, frameworks, and best practices.
• Collaboration and communication skills to work effectively with cross-functional teams and translate technical concepts into business outcomes.
• Attention to detail and quality focus to ensure robust, scalable, and production-ready AI solutions.