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 [email protected] 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
