Senior Data Scientist

Fully RemoteRemotesenior

Data ScientistData

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Quick Summary

Overview

At Silent Eight, we develop our own AI-based products to combat financial crimes that enable things like money laundering, the financing of terrorism, and systemic corruption.

Key Responsibilities

Lead complex R&D initiatives: drive research and innovation in LLMs, NLP, machine learning, and graph analytics, turning advanced techniques into production-grade solutions.

Requirements Summary

Based in Singapore (remote-first team; flexible working environment). Bachelor’s degree, Master or PhD in Data Science, Computer Science, Statistics, or related field.

Technical Tools

bigquerydockerhuggingfacelangchainopenaipythonsparksqlab-testingci-cdlinuxmachine-learningstakeholder-management

At Silent Eight, we develop our own AI-based products to combat financial crimes that enable things like money laundering, the financing of terrorism, and systemic corruption. We’re a leading RegTech firm working with large international financial institutions such as Standard Chartered Bank and HSBC. Join us and help make the world a safer place!

As a Senior Data Scientist, you will play a dual role: technical leader and strategic partner to customers and internal teams.

Responsibilities

~1 min read

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Lead complex R&D initiatives: drive research and innovation in LLMs, NLP, machine learning, and graph analytics, turning advanced techniques into production-grade solutions.

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Own the full data science lifecycle: from discovery, framing, and exploration to deployment, customer delivery, and long-term monitoring.
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Shape ambiguous challenges into well-defined problem statements and design practical, state-of-the-art solutions aligned with business and regulatory needs.
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Engage directly with customers and stakeholders: collaborate to co-design use cases, set success metrics, and ensure solutions are adopted, measurable, and impactful.
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Mentor and guide peers: elevate technical excellence, foster knowledge-sharing, and set best practices for experimentation, testing, and deployment.
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Prototype rapidly: explore new ideas and validate hypotheses quickly, while balancing innovation with scalability and maintainability.
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Ensure production success: design, implement, and optimize pipelines for data integration, validation, monitoring, and retraining to sustain long-term model performance.
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Communicate effectively: distill technical complexity into compelling business insights, helping stakeholders make informed decisions.
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Champion excellence in execution: enforce best practices in MLOps, DataOps, and software engineering to deliver reproducible, maintainable, and scalable solutions.
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Continuously improve tooling and processes that accelerate delivery, experimentation, and collaboration across the data science function.