technologies-expected :

• Python
• Pandas
• scikit-learn
• TensorFlow
• PyTorch

technologies-optional :

• AWS
• Google Cloud Platform
• Microsoft Azure

responsibilities :

• Deliver end-to-end data science projects using classical machine learning (e.g., regression, classification, time series forecasting) and deep learning approaches — from data exploration and modeling to deployment and performance tracking.
• Collaborate closely with business stakeholders to identify opportunities and translate them into measurable data-driven solutions.
• Present complex analytical findings in a clear, actionable way to both technical and non-technical audiences.
• Contribute to analytical planning and ensure alignment with overall business objectives.
• Support presales activities by preparing data/ML solution concepts, participating in client meetings, and estimating project scope and potential value.
• Help shape the Data Science practice by contributing to its strategic development and supporting recruitment initiatives.

requirements-expected :

• Proven experience as a Data Scientist delivering end-to-end projects — from exploratory data analysis to model deployment, performance evaluation, and experimentation (e.g., A/B testing).
• Strong expertise in working with tabular and time-series data, including EDA, feature engineering, predictive modeling, and model evaluation.
• Proficiency in Python and common data-science libraries (e.g., pandas, scikit-learn, TensorFlow/PyTorch).
• Experience leading or mentoring analytics teams and coordinating with business stakeholders.
• Excellent communication skills, able to translate between technical and business perspectives effectively.
• Solid understanding of business processes and data structures.
• Confidence in client-facing settings: gathering requirements, shaping analytical roadmaps, and delivering measurable business value.
• Fluency in English for meetings with international clients.