Surge Global is a digital consultancy that leverages marketing, data, and technology to help businesses grow. As Sri Lanka’s leading digital firm, we employ the best content, creative, design & engineering talent the country has to offer.

Embracing a culture of transparency and equality in the workplace, we’ve built an environment helping Sri Lanka’s best marketing and creative talent thrive. Our team looks at untangling multivariate problems with an open mind and the willingness to constantly test and learn.

With our headquarters in Colombo Sri Lanka, we help scale startups to Fortune 500 companies across the United States, United Kingdom, Australia, and Sri Lanka.

** Please note that this is a 8-month contract position, extendable based on performance and project requirements

Responsibilities:

• Design, develop, and deploy production-grade machine learning models for financial and taxation use cases
• Build and maintain scalable ML pipelines using SAS Viya and Python-based ML frameworks
• Lead feature engineering and data preprocessing for structured financial and taxation datasets
• Train, tune, and validate ML models with a strong focus on accuracy, stability, and explainability
• Implement model monitoring, performance tracking, and drift detection mechanisms
• Collaborate with architects, data engineers, and domain experts to integrate ML models into enterprise systems
• Ensure ML solutions meet regulatory, compliance, and audit requirements
• Conduct code reviews and enforce ML engineering and software development best practices
• Mentor junior ML engineers and contribute to technical decision-making
• Support continuous improvement of ML models and platforms

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, Statistics, or a related field
• 5+ years of hands-on experience in machine learning or applied data science
• Experience working with financial, taxation, risk, or compliance-related data
• Strong hands-on experience with SAS Viya
• Strong proficiency in Python with ML libraries such as Scikit-learn, TensorFlow, or PyTorch
• Solid understanding of supervised and unsupervised learning, feature engineering, and model evaluation
• Experience with SQL and relational databases in financial environments
• Hands-on experience with ML model deployment, monitoring, and MLOps workflows
• Exposure to cloud platforms such as AWS, Azure, or GCP
• Familiarity with Spark or distributed data processing is a plus