全职 Data Scientist - Data Engineer 工作, 薪水, RAPSYS TECHNOLOGIES PTE LTD 公司招聘中

Data Scientist - Data Engineer

Undisclosed

Singapore

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工作地点

职位描述

岗位职责

What We Are Looking For

A degree in Computer Science, Data Science, Statistics, Artificial Intelligence, Computational Linguistics or a related quantitative discipline, or equivalent practical experience.
Demonstrated experience using data science or machine learning to solve real-world problems, preferably involving natural language processing, generative AI, search or information retrieval.
Strong programming skills in Python and working knowledge of SQL, data processing, version control and software-development practices.
Sound understanding of statistics, experimental design, evaluation methodology, sampling, error analysis and model validation.
Experience working with unstructured text or other complex data types, and evaluating machine-learning or generative AI systems beyond a single aggregate metric.
Familiarity with modern NLP and AI concepts such as embeddings, language models, prompt design and model evaluation.
Ability to write maintainable code and work with engineers to bring data-science solutions into production.
Strong analytical, problem-solving and communication skills, with the ability to explain technical findings and trade-offs clearly to diverse audiences.
A proactive and collaborative mindset, willingness to learn, and motivation to improve public services and communications through technology.

The Following Would Be Advantageous

Experience with multilingual NLP, translation quality evaluation, or working with linguists and language reviewers.
Experience with cloud-based AI services, vector search, MLOps, production monitoring or responsible AI practices.

What You Will Be Working On

Work with policy and communications officers, product owners, engineers, domain experts and subject-matter specialists to understand user needs and translate them into clear analytical and machine-learning problems.
Develop and maintain robust evaluation frameworks and datasets for natural language, generative AI and other machine-learning use cases.
Design and conduct experiments to assess and improve model quality, accuracy, consistency, reliability, latency and cost.
Explore and evaluate appropriate models, techniques and emerging technologies, recommending solutions based on evidence, user needs and operational considerations.
Perform systematic error analysis, identify performance gaps across use cases and user segments, and prioritise improvements with the team.
Establish suitable automated and human-evaluation approaches, recognising the limitations and risks of individual metrics and AI-assisted evaluation.
Partner with engineers to integrate validated improvements, define quality checks and monitor performance in production.
Ensure that data, experiments and model decisions are reproducible, well documented and aligned with responsible AI, privacy and security requirements.

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