Tech Stack
AWSAzureGoogle Cloud PlatformHadoopKerasNoSQLPythonPyTorchScalaScikit-LearnSparkSQLTensorflow
About the role
- 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.
Requirements
- 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: Hands-on experience with computer vision techniques (object detection, OCR, facial recognition, document image analysis).
- Preferred: Expertise in deep learning frameworks (TensorFlow, PyTorch, Keras) applied to image-based models.
- Preferred: Familiarity with image preprocessing techniques (augmentation, noise reduction, image normalization).
- Preferred: Understanding of explainable AI in computer vision for compliance-driven use cases.
- Preferred: Ability to translate complex image-based model outputs into product-ready solutions.