Maxis
Linkedin · Posted 10d ago
Data Scientist Specialist
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Indexed description
Are you ready to get ahead in your career?
- We want to empower you to turn your ambitions into achievements.
- We thrive in inclusiveness, diversity and embrace close collaborations for you to create impact for yourself and others.
- Together, we aim to bring the best of technology to help people, businesses and the nation to be ahead in a changing world.
- To realise our vision to become Malaysia’s leading converged solutions company, we are looking for a new talent to innovate and grow with us in a culture that values commitment, performance and possibilities.
What are you accountable for?
- Machine Learning Systems & Architecture: Architect and develop end-to-end ML systems using Python or R. Beyond EDA, focus on building modular, reusable codebases for predictive modeling, recommendation engines, and advanced NLP/Computer Vision architectures.
- Production-Grade AI Deployment: Design and implement robust, scalable AI pipelines and microservices. Focus on transitioning models from experimental notebooks to high-availability production environments (Real-time APIs or distributed batch processing) ensuring low-latency and high throughput.
- ML Ops & Model Lifecycle Management: Implement automated model monitoring and CI/CD for ML (MLOps). Track performance metrics and data drift in production, ensuring systems are self-healing, scalable, and maintain high reliability under varying load conditions.
- Applied AI Research & Innovation: Prototype and integrate state-of-the-art AI advancements - specifically Generative AI, LLMs, and Computer Vision - into existing product stacks to solve domain-specific problems and maintain a technological edge.
- Cross-Functional Systems Integration: Partner with business stakeholders to define technical requirements and collaborate deeply with Data Engineers and DevOps to ensure AI solutions are seamlessly integrated into the broader software ecosystem.
- Engineering Excellence & Documentation: Champion software engineering best practices within the AI team, including version control (Git), containerization (Docker/Kubernetes), and comprehensive system documentation for reproducibility and technical scalability.
- Generative AI Engineering: Architect solutions leveraging Large Language Models (LLMs) through prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG) to deliver high-quality, context-aware AI applications.
- A minimum of 4-7 years of professional experience in Data Science or related technical fields, with at least 3 years dedicated to architecting and deploying production-grade ML models and AI pipelines within complex enterprise ecosystems.
- Highly proficiency in end-to-end AI development, including Gen AI, Computer Vision, and advanced statistical analysis.
- Strong command of SQL, database concepts, and dimensional modeling. Experienced in data transformation methods across various data structures, including relational and unstructured data stores.
- Possesses robust analytical and critical thinking skills.
- A passionate self-starter who is highly dedicated and capable of working independently.
- A strong team player with excellent interpersonal communication skills, proven ability to perform effectively under pressure, and dedicated to delivering results on time.
- A background combining Telecommunication industry knowledge with relevant business experience is highly desirable.
- Once you’ve applied online, our team will carefully review your application. Due to a high volume of applications, we appreciate your patience to allow for a fair and timely review process.
- Should you be shortlisted for the role, we will send you an invitation via email for a digital interview. You can also check on your application status by logging into your candidate account.
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