Data Scientist

About the Role

This position is primarily based in Singapore, with eligibility open to candidates in Hong Kong as well. The Data Scientist at Simpson Thacher & Bartlett LLP will be instrumental in advancing the firm's use of artificial intelligence and data analytics to enhance legal services. The role involves applying advanced statistical techniques, machine learning, and natural language processing to glean insights from both structured and unstructured data, supporting leadership, legal teams, and various operational departments.

Key Responsibilities

• Collaborate with legal and operational teams to leverage data-driven insights for improving client services and firm efficiency.
• Partner with departments such as Finance, Talent, Business Development, and IT to analyze data and develop strategic solutions.
• Create and implement regression and classification models using both traditional and novel data science techniques.
• Fine-tune and deploy advanced pretrained language models (e.g., BERT, Llama4) to enhance NLP tasks like text classification, named entity recognition, summarization, document and clause generation, and interactive Q&A.
• Design document segmentation and embedding methods to support information retrieval and retrieval-augmented generation (RAG).
• Conduct sophisticated quantitative research using machine learning and NLP to detect patterns, relationships, anomalies, and perform classification on large datasets.
• Develop AI workflows and language technology pipelines, including prompt engineering and chaining, tailored to legal practices.
• Create highly visual, interactive reports and user interfaces that present quantitative findings in accessible formats for non-technical stakeholders.
• Keep abreast of the latest advancements in large language models, natural language processing, deep learning, and machine learning to integrate cutting-edge solutions.
• Maintain thorough documentation of development processes, codebases, and best practices to support knowledge sharing and reproducibility.
• Collaborate with technical teams to optimize data pipelines for recurring analyses and data-driven projects.
• Undertake special projects as assigned by senior leadership including the Chief Knowledge & Innovation Officer, Director of Data Analytics, and other executives.

Qualifications & Experience

• Minimum of two years in data science, machine learning engineering, AI, or a related field; alternatively, a PhD in a relevant discipline.
• Strong proficiency in statistical programming languages such as Python or R, and experience with databases like SQL and Pinecone.
• Demonstrated ability to develop and validate linear and nonlinear regression and classification models.
• Expertise with data transformation, scientific computing, and visualization libraries including pandas, scikit-learn, matplotlib, Snorkel, and Seaborn.
• Experience with NLP technologies and frameworks such as Hugging Face Transformers, spaCy, and NLTK is preferred.
• Ability to architect object-oriented machine learning systems beyond exploratory environments such as Jupyter notebooks is advantageous.
• Familiarity with deep learning toolkits like TensorFlow or PyTorch is a plus.
• Competency in version control tools, particularly Git, for effective code collaboration and management.
• Capable of translating business challenges into technical solutions and communicating insights clearly to non-technical audiences.
• Committed to continuous learning and keeping up with emerging trends in data science.
• Experience within the legal sector is strongly favored.

Education Requirements

• Bachelor’s degree is required, ideally in data science, mathematics, statistics, computer science, engineering, finance, or a related discipline.
• A Master’s degree in data science, computer science, statistics, computational linguistics, or engineering is preferred.
• Coursework in deep learning, natural language processing, or information retrieval is an important advantage.