You will develop machine learning and deep learning solutions to address identity risk, account takeover, and payment fraud detection.
Responsibilities
Perform hands-on data exploration, processing, and analysis on massive data sets.
Apply machine learning, deep learning, and reinforcement learning techniques to build solutions for fraud and identity risk assessment.
Research and develop advanced algorithms to solve complex business problems.
Communicate project results to stakeholders and present findings at internal or external conferences.
Contribute to core machine learning capabilities using deep knowledge of ML and software engineering.
Required Skills
Proficiency in Python, TensorFlow, and Keras.
Strong background in machine learning, deep learning, and statistical analysis.
Experience working with large data sets and cloud computing (Hive, Spark, GCP, or Azure).
Proficiency in SQL, Python, Java, or JavaScript.
Experience with software development and machine learning engineering.
Advanced degree (Master or PhD) in a STEM field with 4-6 years of experience, or a Bachelor's degree in a quantitative field with 3+ years of experience.
Knowledge of implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards.
Preferred Skills
Successful completion of assessments in Python, Spark, Scala, or R.
Experience with open source frameworks such as scikit-learn or PyTorch.
Background in optimization models or econometrics.