Posted Apr 29, 2026
As a Data Scientist, your role will involve:
Acting as a thought partner in defining data science strategy and translating it into practical execution roadmaps. - Driving experimentation, validation, and optimization cycles that balance innovation with real-world reliability. - Designing robust data representations that capture temporal, interaction, and anomaly-based patterns. - Implementing scalable machine learning pipelines for real-time analysis and scoring of user sessions. - Collaborating with engineering teams to integrate models into production environments. - Conducting research and staying updated on state-of-the-art approaches in fraud detection, anomaly detection, and behavioral biometrics. Qualifications required for this role include:
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 (Spark, Kafka, Flink, etc.) for streaming/real-time data. - Experience deploying models into production systems (ML Ops, APIs, containerized environments). Additionally, it would be nice to have:
Familiarity with behavioral biometrics, keystroke dynamics, or session replay analysis. - Knowledge of bot detection systems, fraud prevention, or cybersecurity applications. - Experience with big data platforms (Snowflake, Databricks, Hadoop). - Research background in graph-based ML, similarity search, or embedding techniques. Please note that the company may have additional details not included in the provided job description. As a Data Scientist, your role will involve:
Acting as a thought partner in defining data science strategy and translating it into practical execution roadmaps. - Driving experimentation, validation, and optimization cycles that balance innovation with real-world reliability. - Designing robust data representations that capture temporal, interaction, and anomaly-based patterns. - Implementing scalable machine learning pipelines for real-time analysis and scoring of user sessions. - Collaborating with engineering teams to integrate models into production environments. - Conducting research and staying updated on state-of-the-art approaches in fraud detection, anomaly detection, and behavioral biometrics. Qualifications required for this role include:
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 (Spark, Kafka, Flink, etc.) for streaming/real-time data. - Experience deploying models into production systems (ML Ops, APIs, containerized environments). Additionally, it would be nice to have:
Familiarity with behavioral biometrics, keystroke dynamics, or session replay analysis. - Knowledge of bot detection systems, fraud prevention, or cybersecurity applications. - Experience with big data platforms (Snowflake, Databricks, Hadoop). - Research background in graph-based ML, similarity search, or embedding techniques. Please note that the company may have additional details not included in the provided job description.
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