The posting
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 technical concepts to both technical and non-technical stakeholders.
- Proven experience in customer-facing roles is highly valued, particularly in the enterprise tech or financial sectors, ideally serving customers across Asia-Pacific.
- Familiarity with AI-powered product development in industries such as finance, healthcare, or e-commerce.
- Experience with data visualization tools like Tableau, Power BI, or Plotly to present data science findings effectively.
- Knowledge of regulatory requirements in finance, including experience working with financial data feeds and APIs; familiarity with the Singapore regulatory environment (e.g., MAS) is a plus.
Equal Employment Opportunity:
As a global business, we embrace diversity of culture, background, and thought, recognizing it as a key to our success. We are an Equal Employment Opportunity Employer and offer a drug-free workplace.
