עדיין מחפשים עבודה במנועי חיפוש? הגיע הזמן להשתדרג!
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
מלאה
35,000-50,000 ₪
We are hiring a senior individual contributor to join our Data Science team. This is a hands-on role for someone who can take ownership of the full solution, from understanding the problem and building the required data pipelines to developing the model, deploying it, and keeping it reliable in production. You should be comfortable working across data, code, infrastructure, and monitoring independently.
The domain is genuinely hard: noisy sensor and telemetry data, weak and delayed labels, enormous scale, and physical systems that fail in complicated ways. We use whatever technique the problem calls for, from classical machine learning and survival analysis to our own patent-pending deep learning architectures and agentic flows already running in production.
The team moves quickly and operates with a startup mentality: high ownership, short decision paths, and a strong bias toward action. When we encounter a difficult problem, we do not stop at we cant. We ask, How can we?
Moving fast does not mean cutting corners. It means combining resourcefulness and practical judgment with scientific rigor, engineering quality, and production reliability.
We also have proprietary methods for validating results against ground truth, so the feedback loop is real: when a solution works, it ships. You will see your work in production and understand its impact on real fleets.
The domain is genuinely hard: noisy sensor and telemetry data, weak and delayed labels, enormous scale, and physical systems that fail in complicated ways. We use whatever technique the problem calls for, from classical machine learning and survival analysis to our own patent-pending deep learning architectures and agentic flows already running in production.
The team moves quickly and operates with a startup mentality: high ownership, short decision paths, and a strong bias toward action. When we encounter a difficult problem, we do not stop at we cant. We ask, How can we?
Moving fast does not mean cutting corners. It means combining resourcefulness and practical judgment with scientific rigor, engineering quality, and production reliability.
We also have proprietary methods for validating results against ground truth, so the feedback loop is real: when a solution works, it ships. You will see your work in production and understand its impact on real fleets.
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.
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.
This position is open to all candidates.
במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.
מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.
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