Job Responsibilities:
- Data Architecture & Analytics Delivery: Lead the design and implementation of data-analytics platforms and solutions across on-premises, cloud, and hybrid environments.
- Information Delivery & Analytics: Utilize cutting-edge expertise in data preparation, insights, and visualization using BI tools, coupled with advanced data prediction techniques involving AI, ML, and DL.
- AI/ML Operations: Oversee the integration, deployment, and monitoring of AI/ML products and solutions.
- Data Management: Ensure analytics products are ethical, well-controlled, and align with data management best practices.
- Strategic Partnership: Collaborate with Nomura's businesses to shape their information and analytics strategies, driving adoption roadmaps across the organization.
Core Skills Requirements:
- Data Engineering: Design and develop scalable data pipelines to collect and process large datasets from diverse sources.
- Data Modeling & ETL: Build robust physical data models and ETL processes that guarantee data quality, integrity, and accessibility.
- Microservices Development: Create and maintain scalable, fault-tolerant microservices, including efficient server-side APIs.
- Deployment & DevOps: Proficient in CI/CD processes, using tools like Jenkins and Ansible, and experienced in enterprise integration patterns.
- Programming & Orchestration: Hands-on experience with Python, Java, and orchestration tools like Airflow.
- Cloud Technologies: Expertise in cloud platforms such as EC2, EMR, Snowflake, and proficiency in hybrid data architecture design.
- Data Management Methodologies: Skilled in modern data management practices, including building data products and implementing data mesh architectures.
- Machine Learning Expertise: Experience with machine learning libraries and frameworks like LangChain, TruLens, MLFlow, TensorFlow, Scikit-learn, or PyTorch.
- ML Model Deployment: Capable of deploying machine learning models into production and monitoring their performance.
- Data Analysis: Proficient in collecting, cleaning, and analyzing large datasets for training and evaluating machine learning models.
- Cross-Cultural Collaboration: Ability to navigate cultural differences and work effectively with virtual, cross-border teams.
- Adaptability: Comfortable managing multiple demands, shifting priorities, ambiguity, and rapid changes.
- Stakeholder Management: Experience in senior stakeholder management is a plus.
- Communication Skills: Excellent verbal, written, presentation, and interpersonal skills.
- Critical Thinking: Capable of analyzing complex situations and proposing actionable solutions.
- Innovative Approach: Able to challenge existing requirements and current states constructively to maximize value for the firm.
Education and Experience:
- Bachelor's or Master's degree in quantitative fields such as Computer Science, Statistics, or related disciplines.
- 5 years of relevant experience in data engineering, MLOps, or full-stack engineering, preferably within financial organizations.
- Experience working with multi-cultural, multi-disciplinary, globally dispersed teams.
- Relevant certifications in technologies or frameworks are a plus.
Those who are keen for the role and would like to discuss the opportunity further, please click "Apply Now" or email Kin Long at 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.
Morgan McKinley Pte Ltd
EA Licence No: 11C5502 | EAP Registration No: R2095054
