we are looking for a Senior Data Scientist to build and deploy time series forecasting and classical machine learning models that support core business planning and operational decisions. You will own problems end-to-end, from data exploration and feature engineering to model deployment and monitoring, working with large-scale, real-world datasets.
The role emphasizes strong statistical thinking, robust modeling, and production reliability over experimentation with trendy frameworks. Your work will directly impact forecasting accuracy, resource planning, and key business metrics.
Youre welcome to work in our offices in Tel Aviv.
Your responsibilities will include:
Time series forecasting. Design, build, and maintain forecasting models (e.g., demand, usage, capacity). Evaluate performance using appropriate metrics and improve accuracy over time.
Classical ML modeling. Develop and deploy models using regression,Binary classifcatiom , tree-based methods, and other well-established approaches where they provide the best trade-off between performance, interpretability, and maintainability.
Feature engineering & data preparation. Build robust pipelines for data cleaning, transformation, and feature generation (including time-based features, seasonality, and external signals).
Model evaluation & monitoring.Define evaluation frameworks, backtesting strategies, and monitoring to ensure models remain stable and reliable in production.
Production & MLOps. Deploy models into production environments, collaborate on pipeline orchestration, and ensure reproducibility and version control.
Exploratory analysis. Analyze large datasets to identify patterns, anomalies, and drivers of change in time-dependent behavior.
Stakeholder collaboration. Work closely with business and product teams to define forecasting needs, align on success metrics, and translate model outputs into actionable insights.
Cross-domain contribution. Contribute to adjacent data science areas (e.g., LLM-based systems) as needed, ensuring shared ownership and continuity across team responsibilities.
Requirements: Experience as a data scientist working in production environments (5+ years).
Experience with time series modeling and forecasting techniques.
Experience building reliable data pipelines and working with large datasets.
Experience with modern data science tools and ecosystems using Python (e.g., NumPy, Pandas, Scikit-learn, deep learning frameworks).
Strong SQL skills and experience working with large datasets.
Strong foundation in statistics and machine learning fundamentals.
Background in Computer Science, Statistics, Mathematics, Industrial Engineering, Economics, or a related field (Masters degree or equivalent).
Demonstrated ability to take models from idea to production and ensure ongoing value.
Demonstrated ability to apply structured thinking and robust evaluation in forecasting problems.
Strong communication skills and ability to explain model outputs, uncertainty, and limitations.
Working knowledge of spoken and written English.
It will be an added bonus if you have:
Experience working in cloud environments (preferably Azure).
Experience with MLOps tools and production model lifecycle management.
Experience with workflow orchestration tools such as Apache Airflow.
Familiarity with LLM-based systems (e.g., evaluation, RAG pipelines) in applied settings.
This position is open to all candidates.