About the RoleLooking for an experienced and forward-thinking Data Scientist to join my client's growing data science team. This role emphasizes the application of Generative AI and advanced machine learning techniques to solve complex business challenges, particularly within sales and operational domains. You will play a key role in building scalable, secure, and impactful solutions using the AWS tech stack. Key ResponsibilitiesOwn and lead the delivery of end-to-end AI/ML solutions, from conceptualization and development to deployment and iterative enhancement. Design and implement Generative AI use cases to improve sales enablement and drive operational efficiencies.
Collaborate with data engineers, product specialists, and stakeholders to translate business needs into robust and scalable technical solutions. Leverage AWS services (e. g. , Bedrock, S3, Lambda, Redshift, Step Functions) to build, deploy, and maintain ML models and data pipelines. Develop techniques such as document chunking, embeddings, RAG, and prompt engineering to enhance model accuracy and contextual performance. Implement model evaluation frameworks and governance processes to ensure model integrity, reduce hallucinations, and uphold data privacy regulations (e. g. , PDPA). Maintain clear documentation for datasets, model logic, performance tracking, and workflows.
Champion best practices in MLOps, automation, and CI/CD processes on AWS. Actively contribute to knowledge sharing, innovation, and continuous improvement within the team. Required Skills & Qualifications3–5 years of hands-on experience in data science or machine learning, with strong exposure to AWS-based platforms. Bachelor's degree in Computer Science, Engineering, Data Science, or related field. Strong experience with Gen AI and LLM frameworks, including: AWS Bedrock and foundation models (Anthropic Claude, Amazon Titan, Meta Llama). Prompt engineering and model fine-tuning techniques. Retrieval-augmented generation (RAG) and agentic flows. Proficiency in Python, SQL, and Spark. Sound knowledge of ML algorithms:
linear & logistic regression, decision trees, clustering, boosting models. Experience in feature engineering, model evaluation, and hyperparameter tuning. Proven experience with MLOps practices on AWS, including versioning, deployment, and monitoring. Familiarity with modern data warehouse tools (e. g. , Redshift, BigQuery, Snowflake, Hive). Understanding of AI ethics, governance, and compliance with data privacy regulations. Excellent communication and stakeholder management skills. AWS certifications are a strong advantage. Those who are keen for the role and would like to discuss the opportunity further, please click "Apply Now" or email Kin Long at kfok@morganmckinley. com with your updated CV.
Only shortlisted candidates will be responded to, therefore if you do not receive a response within 14 days, please accept this as notification that you have not been shortlisted. Kin Long FokMorgan McKinley Pte LtdEA Licence No: 11C5502 | EAP Registration No: R2095054
Customize your resume to highlight skills and experiences relevant to this specific position.
Learn about the company's mission, values, products, and recent news before your interview.
Ensure your LinkedIn profile is complete, professional, and matches your resume information.
Prepare thoughtful questions to ask about team dynamics, growth opportunities, and company culture.
