Description:
We are seeking a highly skilled Data Scientist with experience of 3-5 years to join our team and lead the development of advanced models that analyze user interaction patterns and measure behavioral dynamics over time. The ideal candidate will have experience working with large-scale interaction data, behavioral analytics, and machine learning for pattern recognition and anomaly detection. This role will focus on designing, developing, and deploying ML models that leverage user interaction data (typing speed, mouse movements, touch patterns, autofill behavior, and behavioral changes over time) to generate meaningful insights and risk scores for understanding user authenticity and identifying behavioral anomalies. Responsibilities Act as a thought partner in defining data science strategy and translating it into practical execution roadmaps. Drive experimentation, validation and optimization cycles that balance innovation with real-world reliability. Design robust data representations that capture temporal, interaction, and anomaly-based patterns. Implement scalable machine learning pipelines for real-time analysis and scoring of user sessions. Collaborate with engineering teams to integrate models into production environments. Conduct research and stay updated on state-of-the-art approaches in fraud detection, anomaly detection, and behavioral biometrics. Qualifications Must-Have: Strong background in Machine Learning, Deep Learning, and Statistical Modeling . Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn, XGBoost). Hands-on experience with time-series data, anomaly detection, or fraud detection . Strong feature engineering skills, especially with high-dimensional and noisy behavioral data. Knowledge of data processing frameworks