AI Data Scientist
Location: Malaysia
Role Summary:
Altera is looking for an AI Data Scientist to drive AI-powered business transformation, advanced analytics, automation, and intelligent data solutions. The role involves working with technical, IT, engineering, and business teams to develop AI solutions, analyze complex datasets, and automate business processes.
Key Responsibilities
- Analyze large datasets to identify trends, patterns, and actionable insights.
- Build Power BI dashboards, reports, DAX measures, and data models.
- Develop Python scripts and automation solutions.
- Build AI solutions using Generative AI, LLMs, Agentic AI, Prompt Engineering, and RAG.
- Work with structured and unstructured enterprise data.
- Use Databricks for data processing, analytics, and AI development.
- Develop AI proof-of-concepts and production-ready solutions.
- Integrate data from multiple enterprise systems and applications.
- Support data quality, governance, validation, and AI performance optimization.
- Collaborate with technical and business stakeholders to identify AI/automation opportunities.
- Present analytical findings and AI solutions to technical and non-technical stakeholders.
Minimum Requirements
- Bachelor's Degree in Data Science, Computer Science, Software Engineering, AI, Machine Learning, Data Analytics, Information Systems, or Computer Engineering.
- Strong Python programming/scripting skills.
- Strong SQL and database knowledge.
- Experience in data analytics, statistical analysis, and visualization.
- Hands-on Power BI, DAX, and data modelling experience.
- Hands-on Databricks experience.
- Knowledge of GenAI, LLMs, Agentic AI, Prompt Engineering, and RAG.
- Experience integrating data from multiple enterprise systems.
- Strong analytical, problem-solving, communication, and stakeholder management skills.
- Ability to independently drive projects from concept to implementation.
Preferred Skills
- AI copilots / enterprise AI application development.
- Azure AI Services, OpenAI, LangChain, or similar frameworks.
- Automated reporting and analytics.
- Customer support, operations, or BI data experience.
- Cloud platforms and modern data architecture.