Senior Data Scientist (Anti-spoofing ML)

Research various methods to analyze multidimensional data and implement advanced machine learning models to identify location spoofing patterns within large datasets.

Create strong pipelines for features and monitoring dashboards to quickly identify deviations in model performance.

Utilize machine learning signals from different products to generate composite risk scores and intelligence at the network level.

Stay updated on the latest fraud patterns and develop expertise in advanced techniques such as graph neural networks, time-series anomaly detection, and LLM.

Take the lead in setting goals, assigning tasks, and ensuring timely completion of projects.

Mentor and assess the work of Data Scientists/Analysts and promote best practices in code review, experimentation, and documentation.

Collaborate closely with engineering managers, data engineers, and other stakeholders to refine requirements, integrate the model into the ML platform, and make a measurable impact.

Interpret model outputs into clear narratives and recommendations through comprehensive reports and presentations for the team.

Document data science workflows, model development, and deployment methodologies, as well as best practices for internal reference and regulatory compliance.

Remain flexible and assist with various projects and initiatives as required in a dynamic environment.

A strong background in machine learning, with expertise in supervised and unsupervised learning techniques, deep learning, and anomaly detection algorithms, cultivated over 5+ years.

Proficient in implementing and fine-tuning machine learning models to ensure accuracy and reliability in fraud detection tasks.

Skilled at analyzing large complex datasets to extract meaningful insights and identify patterns indicating fraudulent behavior, utilizing techniques such as exploratory data analysis, feature engineering, and statistical analysis.

Proficiency in Python and data science-related libraries such as scikit-learn, TensorFlow, PyTorch, pandas, NumPy.

Experience working with cloud platforms, especially Databricks.

Proven track record of designing and automating scalable data processing pipelines and infrastructure.

Effective communication skills and experience collaborating with cross-functional teams, including data scientists, software engineers, and business stakeholders.

Proficient at solving technical challenges related to deploying and managing machine learning models in production, with a focus on troubleshooting, performance optimization, and ensuring system robustness and reliability.

Demonstrated leadership potential through mentoring, leading technical initiatives, and driving innovation within the organization.

Publications or research experience in fraud detection or anomaly detection.

Prior experience in fraud detection.

A PhD degree in Computer Science, Statistics, Mathematics, or a related quantitative field of study.

Learning opportunities and personal development support

Comprehensive health insurance coverage

Competitive compensation package

Lucrative bonuses including 13th month, business performance, and share appreciation rights

Annual salary performance review

Flexibility with hybrid working arrangements and contemporary office in a prime location

Annual company trip and year-end celebration

Regular team-building activities

Access to in-office snacks and beverages

International workplace atmosphere

Weekly in-office yoga classes

PEGASI – IT Recruitment Consultancy | Email: recruit@pegasi.com.vn | Tel: +84 28 3622 8666