Data Scientist

Singapore

Income Insurance Limited

Income Insurance is Singapore's leading provider of Life, Health, Travel & Car Insurance as well as savings, investment and retirement plans. Get started online!

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Data Scientist

Responsibilities:

  • Take the ownership of AI /ML use cases, from design and implementation to continuous enhancement.
  • Serve as a technical specialist on AI, with more focus on Gen AI, within a data science team. Design and develop solutions to improve advisors sales journey and optimize operational efficiency.
  • Collaborate with other data engineers, analysts, data scientists, product specialists, and other stakeholders to build well-crafted, pragmatic, and robust solutions that meet business requirements.
  • Stakeholder management and engagement. Proactively engage with stakeholders to understand their needs and translate them into technical requirements for AI / ML modelling.
  • Maintain documentation of dataset curation, modelling approach, model performance, code changes, and workflows.
  • Demonstrate a strong understanding of data privacy regulations such as PDPA, and AI governance guidelines to ensure compliance.
  • Foster an innovative and growth-oriented mindset, continuously seeking opportunities to enhance AI /ML models and drive improvements across the organization.

Requirements:

  • 3~5 years of experience in data science role with relevant experience in AWS platform.
  • A bachelor’s degree in computer science or equivalent.
  • Familiarity with techniques for Document Chunking, Embedding, Information Retrieval for improving model accuracy and relevance.
  • Expertise in Prompt Engineering for designing and managing effective prompts.
  • Hands-on experience with AWS Bedrock, including deploying solution using foundation models (e.g., Anthropic Claude, Amazon Titan, Meta Llama) and integrating them into scalable applications using APIs and orchestration tools.
  • Experience in designing and applying evaluation frameworks for Gen AI models, including metrics and human-in-the-loop evaluation. Able to implement validation process and guardrails to mitigate risks such as hallucination and misinformation, ensuring the reliability and trustworthiness of AI-generated outputs.
  • Experience in Agentic Flow for maximizing the utility of models, including understanding user intent and context to drive meaningful interactions.
  • In depth knowledge of supervised and unsupervised ML Models – linear & logistic regression, clustering, tree-based models like random forest, bagging and boosting models. 
  • In depth knowledge on feature engineering techniques, hyperparameter tuning and model evaluation. 
  • Proficient in SQL, Python, and Spark. 
  • Proven experience in implementing MLOps practices on AWS.
  • Familiar with Data Warehouses such as Redshift, BigQuery, Snowflake, Hive, and S3.
  • Passionate about technology and always looking to upskill based on new developments in AI space.
  • AWS certifications will be a plus. 
  • Experience in the financial industry, telecommunications, or consulting is preferred. 
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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Job stats:  3  2  0
Category: Data Science Jobs

Tags: AI governance Anthropic APIs AWS BigQuery Claude Clustering Computer Science Consulting Engineering Feature engineering Generative AI LLaMA Machine Learning ML models MLOps Privacy Prompt engineering Python Redshift Snowflake Spark SQL

Region: Asia/Pacific
Country: Singapore

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