About the Role
We are seeking a talented and motivated Data Scientist to join our team and help develop data-driven solutions that tackle real-world business challenges. This is an exciting opportunity to work with large-scale datasets, cutting-edge machine learning models, and advanced analytics tools in a collaborative, innovation-driven environment.
If you have a strong foundation in statistical analysis, machine learning, and a passion for solving complex problems through data, we’d love to hear from you.
Key Responsibilities
* Gather and analyze business requirements to design data-driven solutions and analytical models.
* Design, develop, and implement robust analytics and anomaly detection solutions using best engineering and data science practices.
* Build and integrate data pipelines from multiple internal and external data sources, including real-time sensor streams and environmental datasets, to support advanced analytics and forecasting.
* Develop and validate algorithms for detecting abnormal patterns, drifts, degradations, and other anomalies in time-series data.
* Conduct risk assessments, conceptual design reviews, and ensure analytical solutions align with operational and business needs.
* Execute software development in iterative cycles with continuous monitoring, validation, and performance optimization.
* Deploy analytical models to production environments, ensuring fleet-level validation and stable operation.
* Collaborate with cross-functional teams (engineering, operations, business) and communicate analytical insights clearly to stakeholders.
* Ensure compliance with data governance, quality assurance, and data science standards throughout the development lifecycle.
Required Qualifications
* Bachelor’s degree in Data Science, Computer Science, Statistics, or a related technical field.
* 3+ years of experience in machine learning, time-series analytics, or similar analytical roles.
* Strong programming skills in Python, including experience with libraries for data processing, analytics, and modeling.
* Solid understanding of statistical methods, anomaly detection, drift analysis, and data quality monitoring.
* Experience working with sensor data or other heterogeneous data streams.
* Proficiency with software development best practices, version control (e.g., Git), and deployment workflows.
* Good knowledge of SQL and database management.
* Strong analytical and problem-solving skills with a keen attention to detail.
Preferred Qualifications
* Master’s degree in Data Science, Statistics, or a related quantitative field.
* Experience with advanced machine learning algorithms, ensemble methods, and time-series forecasting techniques.
* Hands-on experience with data preprocessing, feature engineering, and environmental or external data integration for predictive analytics.
* Familiarity with cloud-based development and MLOps pipelines for deploying and maintaining analytical solutions.
* Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) for advanced anomaly detection or forecasting use cases.
* Knowledge of control systems and industrial data analysis is a plus.