Working closely with the CDO, you’ll drive impactful AI initiatives that enhance hyper-personalization, client experience, and operational efficiency. This is an opportunity to build next-gen AI solutions while contributing to enterprise-wide digital transformation in a highly collaborative and innovative environment.

EDUCATION & EXPERIENCE

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Mathematics, or a related field.
• 5 - 8+ years of experience in data science and machine learning, including at least 2 years in a senior or lead role.
• Proven track record of deploying AI models in production, preferably in financial services, tech, or a highly regulated industry.

TECHNICAL SKILLS

• Proficient in Python, SQL, and at least one compiled language (e.g., Java or C++).
• Deep experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and MLOps tools (e.g., Docker, Kubernetes, Grafana, Prometheus, Giskard).
• Strong knowledge of big data tools (Spark, Hadoop) and cloud platforms (AWS, Azure, GCP).
• Experience with streaming data, real-time ML, and model monitoring systems.

DOMAIN KNOWLEDGE

• Ideally some experience in Financial Services, client behavior analytics, risk modeling, or fraud detection.
• Strong understanding of model interpretability and explainability (e.g., SHAP, LIME).

SOFT SKILLS

• Strategic thinker with a hands-on mindset.
• Strong communication and stakeholder management skills.
• Ability to influence and collaborate across functions and geographies.
  If this outstanding opportunity sounds like your next career move, please submit through “Apply Now” or send your resume in Word format to Charlie Kim at resume.sg@pinpointasia.com and put Senior Data Scientist / ML Engineer - Leading Investment Bank in the subject header.
  Data provided is for recruitment purposes only.

KEY RESPONSIBILITIES

• Build and deploy production-grade machine learning models across areas such as personalization, recommendation systems, client segmentation, credit risk, fraud detection, and churn prediction.
• Leverage advanced techniques, including deep learning, reinforcement learning, NLP, and generative AI (e.g., prompt engineering, RAG, agentic AI).
• Design end-to-end ML pipelines using cloud-native, scalable infrastructure (AWS, Azure, GCP) and MLOps best practices.
• Develop and maintain ML infrastructure and automation workflows to enable rapid experimentation, training, and deployment.
• Implement continuous monitoring, performance evaluation, and real-time model optimization.
• Work with Front Office, Marketing, Risk, Compliance, Credit, and IT teams to integrate AI into business processes and client-facing platforms.
• Collaborate with data engineers to build robust, scalable data pipelines and ensure data quality, consistency, and governance.
• Contribute to feature engineering, preprocessing, and real-time data workflows.
• Ensure AI models comply with internal policies and regulatory requirements (e.g., data privacy, fairness, explainability).
• Stay abreast of the latest trends in AI/ML and apply cutting-edge research to real-world problems.
• Share knowledge, mentor junior team members, and contribute to the upskilling of staff across the organization.
• Establish scalable workflows, governance models, and standards for AI model development and industrialization.
• Promote a culture of AI across the firm and collaborate under the Group’s approach with global entities.