Lead and architect advanced analytics and machine learning solutions within the Microsoft cloud ecosystem to support strategic decisions in regulated healthcare environments.
Compensation
This role is compensated through a fixed annual salary. The expected salary range for this position is $170,000.00 – $180,000.00 annually, based on role scope, experience, and market considerations.
Responsibilities
Architect, design, and deploy advanced statistical, predictive, and machine learning solutions that support enterprise‑level decision‑making across healthcare, operations, finance, and customer/member domains.
Own end‑to‑end analytical solution lifecycle, including data discovery, feature engineering, modeling, validation, performance monitoring, retraining strategy, and retirement planning.
Serve as a technical authority on model explainability, fairness, robustness, and regulatory defensibility.
Design analytics solutions natively within the Microsoft cloud analytics stack, ensuring alignment to enterprise data architecture and governance standards.
Partner closely with executive stakeholders, data engineering leaders, and platform owners to translate complex business questions into high‑impact, production‑ready analytics.
Requirements
Master's degree in data science, statistics, mathematics, computer science, engineering, or related field
Principal‑level data scientist responsible for architecting, delivering, and governing enterprise‑grade advanced analytics and machine learning solutions in regulated healthcare environments.
Compensation
This role is compensated through a fixed annual salary. The expected salary range for this position is $170,000.00 – $180,000.00 annually, based on role scope, experience, and market considerations. This position is not eligible for bonus or incentive compensation.
Responsibilities
Architect, design, and deploy advanced statistical, predictive, and machine learning solutions that support enterprise‑level decision‑making across healthcare, operations, finance, and customer/member domains.
Own end‑to‑end analytical solution lifecycle, including data discovery, feature engineering, modeling, validation, performance monitoring, retraining strategy, and retirement planning.
Serve as a technical authority on model explainability, fairness, robustness, and regulatory defensibility.
Design analytics solutions natively within the Microsoft cloud analytics stack.
Partner closely with executive stakeholders, data engineering leaders, and platform owners to translate complex business questions into high‑impact, production‑ready analytics.
Requirements
Master's degree in data science, statistics, mathematics, computer science, engineering, or related field
Architect, design, and deploy advanced statistical, predictive, and machine learning solutions that support enterprise‑level decision‑making across healthcare, operations, finance, and customer/member domains.
Compensation
This role is compensated through a fixed annual salary. The expected salary range for this position is $170,000.00 – $180,000.00 annually, based on role scope, experience, and market considerations.
Responsibilities
Architect, design, and deploy advanced statistical, predictive, and machine learning solutions
Own end‑to‑end analytical solution lifecycle
Serve as a technical authority on model explainability, fairness, robustness, and regulatory defensibility
Design analytics solutions natively within the Microsoft cloud analytics stack
Partner closely with executive stakeholders, data engineering leaders, and platform owners
Requirements
Master's degree in data science, statistics, mathematics, computer science, engineering, or related field
The Manager of AI and Data Engineering will lead the design and implementation of enterprise AI platforms, intelligent agents, generative AI solutions, advanced analytics architectures, and scalable AI engineering practices across the organization.
Responsibilities
Establish technical standards, solution architectures, and engineering patterns for all AI initiatives.
Lead technology decisions involving AI platforms, data platforms, cloud infrastructure, and intelligent automation capabilities.
Evaluate emerging AI technologies and determine enterprise applicability.
Create scalable architectural patterns that balance innovation, maintainability, security, and cost optimization.
Serve as the coach for AI engineering across Armada.
Develop, maintain, and optimize Python pipelines for data ingestion, OCR processing, text extraction, cleaning, normalization, and deduplication.
Run and monitor large-scale batch processing jobs, investigate failures, and implement reliable retry and recovery mechanisms.
Build automated quality checks to identify missing or corrupted files, incomplete OCR outputs, duplicate content, formatting issues, and metadata inconsistencies.
Prepare training-ready datasets through filtering, text segmentation, tokenization, and conversion into agreed data formats.
Manage bulk uploads, downloads, and synchronization between local processing environments and Azure storage; verify transfer completeness and data integrity.
Build and operate Python-based data pipelines for AI training data, including OCR processing, data cleaning, and Azure storage management.
Responsibilities
Develop, maintain, and optimize Python pipelines for data ingestion, OCR processing, text extraction, cleaning, normalization, and deduplication
Run and monitor large-scale batch processing jobs, investigate failures, and implement reliable retry and recovery mechanisms
Build automated quality checks to identify missing or corrupted files, incomplete OCR outputs, duplicate content, formatting issues, and metadata inconsistencies
Prepare training-ready datasets through filtering, text segmentation, tokenization, and conversion into agreed data formats
Manage bulk uploads, downloads, and synchronization between local processing environments and Azure storage; verify transfer completeness and data integrity
Lead data science efforts on high-priority initiatives, build and evaluate machine learning models, and mentor junior team members.
Compensation
The base salary compensation range being offered for this role is $116,000 - $141,000 USD annually.
Responsibilities
Build models end to end
Develop prototypes and products
Lead data science efforts on anomaly detection, forecasting, and predictive modeling
Partner with AI Engineers and Developers
Communicate findings and translate results into actionable business recommendations
Requirements
Bachelor's degree in Computer Science, Information Technology, Data Science, Mathematics, Statistics, Economics, Business Administration, or a related field
A senior data science role focused on AI/LLM systems, agentic product performance, and scalable AI solutions, bridging the gap between technical metrics and business objectives.
Locations (2)
Tel Aviv, Tel Aviv, Israel
Herzliya, Central District, Israel
Responsibilities
Collaborates with relevant partners on defining metrics and frameworks for evaluating complex AI/large language model (LLM) systems and agentic product performance.
Diagnoses complex issues and identifies opportunities to drive improvements within AI/LLM systems.
Prototypes and deploys applications of AI models using containerized environments (e.g., AI Azure Foundry, LangChain).
Optimizes scalable systems that use optimal context engineering (e.g., retrieval-augmented generation) workflows.
Drives AI system roadmaps through continual assessment of model performance and refinement of LLM systems and models.
Serve as the primary architect and guardian of the Artificial Intelligence ecosystem, responsible for end-to-end security of AI architectures within Azure.
Compensation
Base Salary Range: $172,780 to $225,720 annually. An annual employee bonus program
Responsibilities
Design and implement secure-by-design architectures for the entire AI lifecycle, including data ingestion, model training, fine-tuning, and inference.
Lead the security configuration for Azure OpenAI Studio, Azure AI Foundry, and Azure AI Search.
Develop and deploy security controls, including prompt injection mitigations, rate limiting, and content filtering.
Securely implement and oversee Model Context Protocol (MCP) to facilitate safe communication between AI models and local/remote data sources.
Establish and maintain AI security standards based on global frameworks (e.g., NIST AI RMF, ISO/IEC 42001, OWASP Top 10 for LLMs).
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