Lead Data Scientist

About Us

We are hiring on behalf of one of our clients, a leading RegTech SaaS company that helps global enterprises in fintech, banking, and compliance sectors manage risk and regulatory requirements. The company leverages AI and data-driven insights to power its solutions, and now seeks Lead Data Scientists to strengthen its product innovation and analytics capabilities.

Role Overview

As a Data Scientist , you will play a critical role in designing, developing, and deploying machine learning and statistical models to solve complex business and compliance challenges. You will collaborate with engineering, product, and compliance experts to build scalable data-driven solutions that improve product accuracy, efficiency, and customer outcomes.

Key Responsibilities

• Collect, clean, and analyze large structured and unstructured datasets from multiple sources.
• Develop and implement machine learning models for fraud detection, risk scoring, identity verification, and compliance monitoring.
• Conduct statistical analysis, feature engineering, and predictive modeling to extract insights and improve product performance.
• Collaborate with engineering teams to deploy models into production at scale.
• Partner with product teams to design experiments (A / B testing) and evaluate feature effectiveness.
• Research and implement state-of-the-art algorithms in AI / ML relevant to RegTech (e.g., anomaly detection, NLP, computer vision).
• Monitor, evaluate, and continuously improve models for performance, fairness, and compliance.
• Prepare clear documentation, dashboards, and reports to communicate findings to both technical and non-technical stakeholders.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.
• 2–5 years of experience as a Data Scientist or ML Engineer (preferably in SaaS, fintech, or RegTech).
• Proficiency in Python, R, or Scala , with strong knowledge of libraries such as scikit-learn, TensorFlow, PyTorch, or similar.
• Strong understanding of statistics, probability, and machine learning techniques (classification, clustering, NLP, anomaly detection).
• Experience working with SQL and NoSQL databases .
• Knowledge of big data tools (Spark, Hadoop, or similar) is a plus.
• Experience deploying ML models to production environments (AWS, GCP, or Azure).
• Strong analytical, problem-solving, and communication skills.

Preferred Skills