Enterprise Data Scientist

As an 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. • Bachelor’s or Master’s 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 DynamoDB, as well as search engines like Elasticsearch and vector databases for semantic search (e.g., Pinecone, Weaviate). • Hands-on experience with data ingestion, data wrangling, and data pipeline orchestration using tools like Apache Kafka, Apache Spark, Airflow, and distributed computing frameworks like Dask and Ray. • Experience with advanced data science methodologies, including ensemble learning, deep reinforcement learning, transfer learning, and deploying large pre-trained models for real-time inference and production. • Ability to design, prototype, and deploy NLP models for a range of applications, from information retrieval to sentiment analysis, chatbots, and question answering systems. • Demonstrated success in delivering solutions in complex, fast-paced environments with a focus on customer satisfaction and technical excellence. • Strong communication skills, with the ability to explain complex…