Job Description

GEN AI Data Scientist Data Analytics

Responsibilities:

Take ownership of AI/ML use cases, from design and implementation to continuous enhancement.
Act as a technical specialist in AI, primarily focusing on Generative AI. Design and develop solutions to address various business challenges, particularly those related to optimizing operational efficiency.
Collaborate with other data engineers, analysts, data scientists, product specialists, and 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 modeling.
Maintain documentation of dataset curation, modeling 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:

35 years of experience in a data science role, with demonstrable expertise in the AWS platform. Experience with Microsoft Copilot Studio is a plus.
Bachelor's degree in computer science or equivalent.
Familiarity with techniques for Document Chunking, Embedding, and 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 solutions using foundation models and integrating them into scalable applications using APIs and orchestration tools.
Experience in Agentic Workflow 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.
Proficient in SQL, Python, and Spark.
Proven experience in implementing MLOps practices on AWS.
Familiar with Data Warehouses such as Redshift, Hive, and S3.
Passionate about technology and always looking to upskill based on new developments in the AI space.
AWS or Microsoft certifications will be a plus.
Experience in the financial industry, telecommunications, or consulting is preferred

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