לוח משרות דרושים הייטק דאטה בהרצליה, לחיפוש זה נמצאו 15 משרות

Requirements:
2+ years of demonstrated experience leading a technical team.
Proven experience designing and operating enterprise-scale data platforms/lakes/warehouses.
Deep familiarity with big data ecosystems and monitoring/log analysis tools used for security telemetry, distributed tracing, and incident response.
Strong software engineering fundamentals with high level proficiency in one or more programing languages (Python, Java, Go, Rust, etc.).
Proficiency in system design, development tools and best practices for building scalable and maintainable solutions.
It would be great if you also have:
Experience applying AI to observability or security workflows (e.g., LLM-powered agents, MCP server architecture, anomaly detection or automated log parsing).
Production experience with Elasticsearch and Splunk for log aggregation, full-text search and security analytics.
Background working directly with cybersecurity data sources.

Requirements:
B.Sc in Industrial engineering and management/ Information System/ Computer Science or equivalent [a must]
2+ years of experience in project management, or technical support
A can-do attitude, willingness to tackle problems or new tasks and go the extra mile
Ability to work within a team or individually
Experience in a product, project, or technical coordination role
Knowledge/experience in Python and SQL
Understanding of APIs, JSON, webhooks, authentication, and system integrations
Experience working with data and translating business requirements into KPIs, reports, and dashboards
Understanding of Generative AI, LLMs, AI agents, and AI-powered automation
Ability to identify opportunities to apply AI and automation to improve business and technical processes
Familiarity with working with AI APIs and AI-based tools
Good mathematics, problem-solving, interpersonal and organizational skills
Ability to drive tasks to completion in a cross discipline environment
Detail-oriented with a focus on delivery and follow-through
Fluent in English and Hebrew (spoken and written) [a must]
Familiarity with agile methodologies and tools like Jira and Trello [Advantage]
Previous experience in an adtech, media, or SaaS company [Advantage]

Requirements:
B.Sc./M.Sc. in Computer Science, Statistics, Applied Mathematics, or related field
5+ years of hands-on experience applying machine learning in production environments
Strong Python skills and experience with ML libraries such as scikit-learn, XGBoost, LightGBM, or CatBoost
Proven experience with feature engineering, model selection, and evaluation in real-world scenarios
Strong statistical understanding and ability to reason under uncertainty
Experience working with messy, high-dimensional, or time-series data
Ability to independently own problems and drive them to production

Requirements:
B.Sc. in Computer Science, Engineering, or a related field.
4+ years of hands-on experience in data engineering, backend software engineering, or data infrastructure.
Python Expertise: Extensive experience and strong proficiency in Python - a must-have.
Data at scale: Proven experience designing and operating data pipelines and storage/serialization formats (e.g. Parquet, HDF5, Arrow) for large datasets.
System Architecture: Proven ability to build robust, well-tested data tooling and integrate complex software components into reliable pipelines.

Requirements:
MSc in Computer Science or an engineering-related quantitative field, such as Electrical Engineering, Statistics, Physics, or Applied Mathematics. A PhD is an advantage.
At least eight years of industry experience in a related data science or machine learning role.
Proven experience taking machine learning solutions to production, not only building prototypes and notebooks, but deploying, monitoring, maintaining, retraining, and troubleshooting what you shipped.
Strong experience building reliable, performant, and well-tested data pipelines over very large datasets.
Strong experience with Spark, distributed computing, SQL, and modern big data platforms. Databricks experience is a significant advantage.
Practical experience with automated testing and deployment, cloud-based systems, workflow orchestration, monitoring, and production troubleshooting.
Experience delivering LLM or agentic applications to production, including evaluation, guardrails, and cost and latency trade-offs.
Strong foundations across statistics, experimental design, classical machine learning, and modern deep learning.
Excellent Python skills and fluency with PyTorch.
Demonstrated ability to work comfortably across data science, data engineering, model deployment, and production systems.
Demonstrated ability to lead projects independently, make sound decisions under ambiguity, and determine what should-and should not-be built.
A proactive and resourceful mindset. You naturally look for viable solutions rather than stopping at constraints.
Very good English communication skills, with the ability to present clearly to business stakeholders and C-level audiences.