Senior Associate , Decision science

Responsibilities

• Lead the end-to-end lifecycle of strategic AI/ML initiatives, from problem definition and solution design to production deployment and business adoption.
• Partner with Product, Engineering, Sales, and business stakeholders to identify high-impact opportunities and translate ambiguous business problems into scalable AI-driven solutions.
• Design, develop, evaluate, and productionize machine learning, deep learning, and Generative AI models, including agentic AI workflows where appropriate.
• Own technical solution architecture, model selection, experimentation strategy, and production deployment while ensuring scalability, reliability, and maintainability.
• Drive AI innovation by identifying opportunities to leverage LLMs, AI agents, recommendation systems, optimization techniques, and predictive modelling to improve business outcomes.
• Collaborate closely with Engineering and Infrastructure teams to build robust ML pipelines, automate model retraining, monitoring, and continuous evaluation.
• Define success metrics, conduct experiments, analyse outcomes, and communicate insights and recommendations to senior leadership.
• Mentor junior engineers through technical guidance, code reviews, and best practices in AI/ML development.
• Influence technical direction across multiple projects while driving engineering excellence and reusable AI platforms.

Qualifications

• Basic Qualifications: Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative discipline. 7+ years of industry experience applying machine learning and statistical techniques to solve complex business problems. Programming skills in Python with experience building production-grade ML applications. Experience building scalable ML pipelines and integrating models into production systems. Experience working with large-scale structured and unstructured datasets using Spark, SQL, or distributed computing frameworks. Communication skills: ability to influence technical and non-technical stakeholders.
• PREFERRED QUALIFICATIONS: Experience building production-grade Generative AI applications using LLMs, Retrieval-Augmented Generation (RAG), AI agents and orchestration frameworks. Experience in deploying machine learning models in Azure cloud. Experience with distributed data systems such as Hadoop and related technologies (Spark, Presto, Pig, Hive, etc.). Background in any one of programming language (C#, Java, PHP, JavaScript). Experience designing multi-agent systems and autonomous AI workflows. Hands-on experience with modern AI frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or equivalent. Deep understanding of technical and functional designs for relational and MPP Databases. Familiarity with CI/CD, MLOps, model monitoring, observability, and production AI governance. Experience mentoring scientists and leading cross-functional technical initiatives. Publications, patents, or contributions to open-source AI projects are a plus.