Company Summary Veridium is a leading provider of end-to-end biometric authentication solutions for enterprises deploying biometrics as part of their access and identity management security strategies.
Powered by an unmatched knowledge of biometrics R&D, Veridium solutions increase convenience and security, reduce fraud, and cut costs associated with passwords and traditional multi factor authentication.
We are looking for highly-motivated, forward-thinking talent to add to our growing team. VeridiumID platform it is composed by :
Mobile App (Android and iOS)
Integration points with AD and Citrix infrastructure
User Behavior Authentication
Mobile native applications (Android and iOS)
VeridiumID Service Java REST API
VeridiumID Admin Console (AngularJS application)
Persistence Layer / Data processing Zookeeper, Kafka, Spark / Tensor Flow, Cassandra (+Lucene Index)
Deployment Ansible Scripts / python
Requirements Min requirements :
MS in Computer Science / Electronics / Electrical Engineering or a related technical discipline
Master in Artificial Intelligence
Having as many of the following skills represents an advantage :
Knowledge of different types of machine learning algorithms (SVM, Kernel Ridge Regression, Random Forest, PCA, k-means, etc.
and know how to use them in practice
Good understanding of neural network theory and how to train, test and evaluate modern architectures, e.g. convolutional neural networks, recurrent neural networks, auto-encoders, etc.
Ability to combining different types of models and architectures in order to improve the performance.
Knowledge of tuning models' parameters, e.g. using grid search.
Feature engineering. Being able to understand the data that you are working with and extract useful features.
Statistics and probability : Know how to evaluate a model or a solution in terms accuracy, precision, recall or other performance metrics.
Understand probabilistic models like Naive Bayes, Hidden Markov Models, ROC curves, etc
Signal processing : Use different types of signal processing methods for extracting relevant features, e.g. Discrete Fourier Transform.
Read and understand different scientific papers in order to find ideas that can be applied to our solution.
Being able to implement the solution in a distributed environment, e.g. Spark and Kafka.
Knowledge of python and working experience with libraries such as tensorflow, keras, scikit-learn, numpy, pandas.
Research and develop solutions for user behavior analysis