About the Role

We are looking for 4 skilled and motivated Data Scientists to join our team for a 6-month project focused on developing data-driven solutions to solve real-world business challenges. This is a project-based opportunity, ideal for professionals who enjoy working on focused, high-impact initiatives with clear objectives and timelines.

You’ll work with large-scale datasets, cutting-edge machine learning models, and advanced analytics tools in a fast-paced, collaborative environment. While the initial engagement is limited to 6 months, there may be opportunities for continued collaboration or growth beyond the project, depending on performance and business needs.

If you have a strong foundation in statistical analysis, machine learning, and a passion for turning data into actionable insights, 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.