AI Data Scientist

Avensys Consulting; Singapore; On-site; SGD 90,000 - 120,000; Full time

Job summary

A leading technology consulting firm in Singapore is seeking an experienced AI Data Scientist to join their advanced analytics and AI team. The ideal candidate will have a strong background in machine learning, particularly in fraud detection systems and AWS SageMaker, to design and optimize predictive models in regulated industries like healthcare and finance. This role offers the opportunity to drive data-driven decisions and collaborate with cross-functional teams.

Qualifications

• 5+ years of experience in data science, machine learning, or applied AI roles.
• Hands-on experience with AWS SageMaker for model training, tuning, and deployment.
• Proven track record in fraud detection or anomaly detection systems.

Responsibilities

• Design, develop, and deploy machine learning models using AWS SageMaker.
• Develop and maintain fraud detection models leveraging ensemble methods.
• Conduct exploratory data analysis (EDA) and feature engineering.

Skills

Machine learning model development

Fraud detection systems

Cloud-based ML deployment (AWS SageMaker)

Programming in Python

Data visualization

Education

Bachelor’s or Master’s degree in Computer Science, Statistics, Data Science, or related field

Tools

AWS SageMaker

Python libraries (pandas, scikit-learn, PyTorch/TensorFlow)

SQL

Tableau

QuickSight

Plotly

Job description

Job Title : AI Data Scientist

Overview

NCS is seeking an experienced and highly analytical Data Scientist to join our advanced analytics and AI innovation team. The ideal candidate will have deep expertise in machine learning model development, fraud detection systems, and cloud-based ML deployment (AWS SageMaker). You will work closely with cross‑functional teams to design, build, and optimize predictive models that drive data‑driven decisions, particularly within regulated industries such as healthcare, banking, or insurance.

Key Responsibilities

• Design, develop, and deploy machine learning models using AWS SageMaker and related AWS services (e.g., Glue, Lambda, S3, Athena).
• Develop and maintain fraud detection models leveraging ensemble methods (Random Forest, Gradient Boosting, XGBoost) and anomaly detection techniques.
• Conduct exploratory data analysis (EDA), feature engineering, and data quality assessments across large structured and unstructured datasets.
• Collaborate with data engineers to build scalable pipelines for training and batch inference.
• Evaluate and tune models for accuracy, precision, recall, and real‑time performance in production environments.
• Work closely with stakeholders to translate business challenges into machine learning solutions, providing actionable insights and recommendations.
• Document processes, methodologies, and performance metrics to ensure auditability and compliance (especially in healthcare or financial contexts).
• Stay current with advancements in ML, deep learning, and AWS AI services to continuously enhance capabilities.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Statistics, Data Science, Applied Mathematics, or related field.
• 5+ years of experience in data science, machine learning, or applied AI roles.
• Hands‑on experience with AWS SageMaker for model training, tuning, and deployment.
• Strong programming proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow) and SQL.
• Proven track record in fraud detection, risk scoring, or anomaly detection systems.
• Experience with cloud data architectures and integrating ML workflows into production.
• Proficient in data visualization and communication of model results using tools such as Tableau, QuickSight, or Plotly.

Preferred Qualifications

• AWS Certified Machine Learning – Specialty or AWS Certified Data Analytics – Specialty.
• Experience in healthcare analytics (claims fraud, patient risk scoring, readmission prediction) or financial/banking fraud detection (transaction monitoring, AML).
• Familiarity with regulatory requirements such as HIPAA, PCI‑DSS, or GDPR.
• Exposure to MLOps frameworks (CI/CD for ML pipelines, model monitoring, retraining).
• Knowledge of NLP, deep learning, or graph analytics is a plus.