Data Scientist & ML/AI Engineer

We are looking for a Data Scientist & ML/AI Engineer to join a high-tech company based in Ra’anana!
This is a temporary position with possible extension.

We are looking for a highly skilled Data Scientist & ML/AI Engineer hands-on leaders to join our eSIM team for a project focused on building advanced AI models for fraud detection and anomaly spotting. The ideal candidate will have strong expertise in data science, machine learning, and AI engineering, with a proven track record of delivering production-ready solutions. We highlight that most used data will be unlabelled logs; the role requires to be familiar with unsupervised learning techniques in adition to semi-supervised and supervised approaches.
The project consist of developing a fraud and anomaly detection module by leveraging logs and information collected by our existing platform. The goal is to design, implement, and optimize algorithms and machine learning models to detect anomalies in eSIM related transactions (e.g. profile downloads).
You are expected to lead the AI project through the complete lifecycle including defining problems, designing solutions, and implementing models that will be integrated to the eSIM Cloud product to drive real business impact. Our team prefers the usage of MS Azure environment.

What will your tasks look like?

Data Preparation, ETL & storage: Collect, clean, and transform large datasets from multiple sources into structured formats suitable for modeling.
Model Development: Select appropriate algorithms (e.g., neural networks, gradient boosting, anomaly detection) and frameworks (TensorFlow, PyTorch, Scikit-learn). Some key words:
- Clustering: K-Means, DBSCAN, Hierarchical Clustering
- Detection: Autoencoders, Isolation Forest, One-Class SVM, BERT models
Training & Evaluation: Train models on historical and real-time data, evaluate performance using metrics such as precision, recall, reconstruction error.
Model Comparison & Improvement: Benchmark different approaches, optimize hyperparameters, and improve accuracy and efficiency.
Deployment & Monitoring: Implement models in production environments, ensure scalability, and set up continuous monitoring for performance and drift.
Collaboration: Work closely with data engineers, software developers, and business stakeholders to align technical solutions with business objectives.
Cost Optimization: Design solutions that balance performance and cost efficiency, leveraging cloud resources effectively.
Document the development and design decisions.

Areas of Expertise Needed:

Education: Master’s or Ph.D. in Computer Science, Data Science, AI, or related field.
Experience: Minimum 4 years in machine learning and AI engineering, with hands-on experience in fraud detection or anomaly detection.
Product Domain Skills:
- Real-Time Detection: Experience designing models for real-time anomaly detection in high-throughput environments.
- Transaction & Log Analysis: Expertise in analyzing large volumes of transactional logs and network events to identify anomalies and suspicious behaviors.
- Telecom & eSIM Ecosystem: Understanding of eSIM lifecycle management, including profile downloads, activation, and provisioning processes.
- Fraud Patterns in Telecom: Familiarity with fraud scenarios such as SIM swap, identity theft, and unauthorized profile downloads in mobile networks.
Technical Skills:
- Programming: Python (mandatory), Database querying.
- Frameworks & Libraries: TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, Python Outlier Detection library.
- Data Engineering: ETL pipelines, data preprocessing, feature engineering.
- Cloud Platform: Azure ML, Fabric, Azure ecosystem.
- MLOps Tools: MLflow, Kubeflow, Docker, Kubernetes for model deployment and monitoring.
- Visualization: Power BI or similar tools.
Soft Skills: Strong analytical thinking, problem-solving, and ability to communicate complex concepts clearly.

Hybrid work (3 days from the office).