Enterprise Data Scientist

As a Enterprise Data Scientist, you will leverage cutting-edge technologies and methodologies to deliver data-driven insights and solutions for complex customer needs. You will work on end-to-end solutions, including building Proof of Concepts (POCs) and production-grade agentic AI systems, professional services, and integrating third-party technologies with client systems. This role is pivotal to ensuring the successful implementation of data science-driven products and capabilities, with a key focus on AI, machine learning, generative AI, and Natural Language Processing (NLP). You will collaborate closely with cross-functional teams, delivering innovative solutions to customers in highly dynamic, data-intensive environments.

Role & Responsibilities:

Lead and execute complex customer engagements across the Asia-Pacific region, utilizing specialized expertise in AI, Machine Learning, Generative AI, and NLP, including building POCs, agentic AI workflows, integrations, and deployments with customer workflows.Apply a combination of technical, product, and data science expertise to co-create solutions that address specific customer needs, including ideation, clarification, technical design, and documentation.Lead detailed customer presentations for complex technical propositions, focusing on explaining advanced data science concepts and AI/ML solutions in an accessible way.Manage relationships with internal and external stakeholders, ensuring that project and customer-specific technical requirements are captured, refined, and translated into actionable solutions.Oversee and contribute to the development of Proof of Concepts, ensuring integration with customer workflows and systems.Lead the technical design and implementation of AI and machine learning solutions that integrate with existing client infrastructure.Drive the adoption of advanced data science and AI technologies to deliver high-value solutions.Develop and present strategies for scaling AI solutions, utilizing cloud platforms (Azure, AWS, GCP) for production-ready deployments.Design and implement Retrieval-Augmented Generation (RAG) pipelines and agentic AI systems that orchestrate multiple tools and models to solve customer problems.Establish LLMOps practices, including evaluation, guardrails, and observability, to ensure safe, reliable, and responsible deployment of generative AI solutions in line with regulatory expectations.Lead and mentor junior team members across the Singapore and broader Asia-Pacific team, fostering a collaborative environment for continuous learning and technical growth.

Qualifications and Experience:

10+ years of experience in data science or a related field, with a focus on AI, machine learning, and NLP, preferably in a senior technical or leadership role.Bachelors or Masters degree in Computer Science, Engineering, Data Science, or related field. A Ph.D. in a relevant field is a plus.Expertise in Natural Language Processing (NLP) and Generative AI with a deep understanding of the latest LLM landscape, including transformer-based architectures such as BERT and T5, and current frontier and open-weight models (e.g., GPT-5.x, Claude 4/5, Gemini 2.x/3.x, Llama 4, DeepSeek, Qwen) that are driving the evolution of NLP and agentic AI applications.Hands-on experience building agentic AI systems, including multi-agent orchestration (e.g., LangGraph, AutoGen, CrewAI), tool/function calling, and integration via the Model Context Protocol (MCP).Practical experience with Retrieval-Augmented Generation (RAG), including chunking strategies, embedding models, hybrid search, and retrieval evaluation.Experience fine-tuning and adapting large models efficiently, using techniques such as LoRA/QLoRA, parameter-efficient fine-tuning (PEFT), quantization, and distillation, along with LLMOps practices for prompt evaluation, guardrails, hallucination testing, and observability (e.g., LangSmith, RAGAS, Arize).Awareness of responsible AI and governance requirements, including model risk management and emerging regulation (e.g., EU AI Act) as applicable to financial services.Extensive experience with advanced machine learning and deep learning frameworks such as PyTorch, TensorFlow, Hugging Face, and JAX for NLP, multimodal (vision-language), and other advanced AI tasks.Deep knowledge of cloud services (AWS, GCP, Azure) and their use in data science workflows, particularly for deploying machine learning models at scale.Expertise in Python, with advanced knowledge of modern data science and machine learning libraries such as Pandas, NumPy, SciPy, scikit-learn, spaCy, as well as cutting-edge NLP frameworks like Hugging Face Transformers, Datasets, and NLTK for efficient model training, fine-tuning, and data preprocessing.Strong programming skills in Python, R, and SQL, with advanced proficiency in handling large-scale data using distributed data systems like Apache Spark, cloud-native NoSQL databases such as MongoDB, Cassandra, and .