Scienziato Data Science

About the Role

We are looking for a Data Scientist with strong expertise in time-series analytics and anomaly detection to design and deploy data-driven solutions for complex, data-intensive systems.

In this role, you will work on advanced analytical models using heterogeneous data sources, including real-time sensor streams and environmental datasets, supporting forecasting, monitoring, and risk mitigation use cases.

You will collaborate closely with engineering, operations, and business teams to translate requirements into robust, production-ready analytics solutions.

Key Responsibilities

• Gather and analyze business and technical requirements to design data-driven analytical solutions.
• Design, develop, and implement analytics and anomaly detection models following best practices in data science and software engineering.
• Build and integrate data pipelines from internal and external sources, including

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  eal-time sensor data.
• Develop, validate, and optimize algorithms to detect anomalies, drifts, degradations, and abnormal patterns in time-series data.
• Perform risk assessments and conceptual design reviews, ensuring alignment with operational and business needs.
• Develop software in iterative cycles, with continuous validation, monitoring, and performance optimization.
• Deploy analytical models to production environments, ensuring stability and fleet-level validation.
• Collaborate with cross-functional teams and communicate insights clearly to technical and non-technical stakeholders.
• Ensure compliance with data governance, quality, and data science standards throughout the development lifecycle.

Required Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, or a related field.
• 3+ years of experience in machine learning, time-series analytics, or similar analytical roles.
• Strong Python programming skills and experience with data processing and modeling libraries.
• Solid understanding of statistical methods, anomaly detection, drift analysis, and data quality monitoring.
• Experience working with sensor data or heterogeneous data streams.
• Familiarity with software development best practices, version control (Git), and deployment workflows.
• Good knowledge of SQL and database management.
• Strong analytical mindset with excellent problem-solving and attention to detail.

Preferred Qualifications

• Master’s degree in Data Science, Statistics, or a related quantitative discipline.
• Experience with advanced machine learning, ensemble methods, and time-series forecasting.
• Hands-on experience with data preprocessing, feature engineering, and integration of external or environmental data.
• Familiarity with cloud platforms and MLOps pipelines.
• Experience with deep learning frameworks (TensorFlow, PyTorch).
• Knowledge of control systems or industrial data analytics is a strong plus