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
