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 real-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
