לוח משרות דרושים Senior Data Scientist משרה מלאה, לחיפוש זה נמצאו 37 משרות

Requirements:
Solid experience in data science, with a focus on image analysis and text reading.

Proficiency in programming languages such as Python and familiarity with relevant libraries (e.g., TensorFlow, PyTorch, OpenCV).

Strong understanding of statistical modeling, machine learning, and deep learning algorithms.

Experience working with large-scale datasets and applying data preprocessing techniques.

Excellent problem-solving skills and the ability to work independently as well as in a team environment.

Strong communication and presentation skills, with the ability to convey complex concepts to non-technical stakeholders.

Requirements:
5+ years of experience in Machine Learning, working with tabular data on predictive modeling
Track record of translating research into business impact
MSc or PhD in a related field- Math/EE/CS/Physics/Stats
Great interpersonal skills, and strong collaborator
Good applied statistics skills, such as distributions, statistical testing, regression, etc.
Proficient in Python and the Data Science Stack
Independent and fast learner that can work alone and in teams
Ability to learn and explore new fields of expertise
Knowledge in some of the following can be an advantage: optimization, risk and finance.

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.

Requirements:
- MSc. or PhD in Statistics, Applied Math, Physics, or a closely related quantitative field.
- Real research experience - a track record of driving original investigations end-to-end (academic research, a research-heavy PhD, industry R&D, or equivalent), not
only applied ML delivery.
- Strong mathematical or statistical problem-solving instincts - able to model a messy real-world system from scratch, not just apply a library.
- Production deployment experience: monitoring, CI/CD, data validation, reproducibility.
- Ability to independently initiate, plan, and drive entire research directions.
- Team player, positive, driven, independent, fast learner.
Advantages
- Depth in classical statistics, causal inference, or applied probability.
- Deep learning experience.
- Clean coding and repository-maintenance

Requirements:
Minimum qualifications
5+ years of experience in leading data science and machine learning projects, with significant hands-on work building LLM-based applications or AI agents.
Deep practical experience with LLMs (prompt engineering, fine-tuning, model selection, and understanding trade-offs across providers and model families).
Knowledge of programming languages that are used in AI research, such as Python, and experience with AI frameworks (e.g., Hugging Face, LangChain, OpenAI, scikit-learn, TensorFlow, PyTorch).
An M.S. or Ph.D. degree in computer science, statistics, or related field OR equivalent work experience.
Preferred qualifications:
Experience with distributed cloud systems and hands-on familiarity with cloud-native architectures at scale.
Strong knowledge of deep learning models and common model architecture such as transformer models.
Knowledge of cybersecurity principles, attack vectors, and defense mechanisms.
Excellent communication (both written and verbal) and presentation skills.
Ability to work independently in a fast-paced environment and come up with creative solutions to challenging problems.

Requirements:
M.Sc. or Ph.D. in Computer Science, Statistics, Mathematics, or related field.
6+ years of hands-on data science experience, including direct work with lifecycle marketing, personalization, or customer analytics.
Advanced expertise in statistics, causal inference, and experimental design.
Proven track record of implementing reinforcement learning / multi-armed bandit models in production.
Strong software engineering skills in Python, SQL, and ML frameworks
Exceptional communication skills - able to influence internal teams and present to executive-level customers.
Entrepreneurial mindset: thrives in uncertainty, challenges assumptions, and pushes for impactful solutions.

Requirements:
Education: MSc or PhD in Data Science, Computer Science, Mathematics, Statistics, or a related engineering field.
Experience: 4+ years of proven experience as a Data Scientist solving complex business problems with state-of-the-art algorithms.
Deep Learning: Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow, Keras).
Engineering & Production: High-level software engineering expertise, with a strong track record of deploying, maintaining, and scaling models in rigorous production environments.
Strong analytical thinking: Take a vague business question, translate it into a testable hypothesis, build the infrastructure to run an experiment, and draw rigorous conclusions from the results.
Massive Scale: Experience working with billions of records and big-data frameworks (e.g., Spark, Ray, Dask, Airflow).
Domain Expertise: Proven experience building and optimizing applied recommendation systems.
AI development: Fluency with modern AI coding tools and practices (e.g. Cursor, Claude Code, Copilot) and an eagerness to integrate them into your daily workflow
Leadership & Soft Skills: Demonstrated experience leading machine learning projects. You are a highly analytical team player capable of handling multiple tasks simultaneously in a fast-paced environment.
You might also have
LLM & Agentic Frameworks: Experience building applications with modern AI tools and frameworks (e.g., LangChain, LangGraph).
AI Observability: Hands-on experience with LLM observability, evaluation, and tracing frameworks (e.g., Langfuse).
MLOps & Infra: Experience working with MLOps tools (e.g., MLflow, Kubeflow, Weights & Biases, SageMaker) and feature stores (e.g., Feast, Tecton).
Additional information
Relocation support is not available for this position
Work visa/immigration sponsorship is not available for this position
Benefits
At our company, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.

Requirements:
4+ years of experience building machine learning solutions end-to-end, from research and experimentation through production.
MSc or PhD in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field.
Experience developing machine learning models for real-time bidding (RTB), recommendation systems, ranking, pricing, or other large-scale optimization problems.
Strong programming skills in Python and experience working with production-grade machine learning systems.
Strong foundation in machine learning, statistical modeling, experimentation, and data analysis.
Bonus points if you have:
Experience with deep learning models in large-scale recommendation or advertising systems.
Experience using AI development tools (such as Claude Code or Cursor) to accelerate experimentation and development.