Senior Data Scientist – Time Series & Cloud Analytics (Remote)

We are seeking a Senior Data Scientist with deep expertise in Time Series Analysis, Statistical Modeling, and Cloud-based Data Engineering to join our high-impact team tackling some of the toughest data challenges in the oil & gas industry. This role involves solving complex real-world problems using large-scale data from sensors, operations, and infrastructure systems in a Cloud-based environment.

You will work on a technically demanding project that requires exceptional analytical rigor, cloud engineering skills, domain understanding, and creativity.

Key Responsibilities:

• Lead the design and development of advanced time series forecasting models for industrial operations and equipment.
• Analyze large-scale sensor data, SCADA systems, and historical logs to extract actionable insights.
• Develop and maintain robust statistical and machine learning models with a focus on scalability, cloud performance, and reproducibility.
• Build and optimize ETL pipelines using tools like Apache Airflow, integrating data from diverse sources into cloud data lakes (e.g., AWS S3, Azure Data Lake Gen2).
• Collaborate with cross-functional teams including data engineers, domain experts, and business stakeholders.
• Develop dashboards and visualizations using Power BI, Tableau, and SQL for business-facing reporting.
• Work with APIs to integrate data services and models into enterprise systems.
• Apply data modeling and model performance tuning best practices in a production-grade environment.
• Use Git/GitHub for version control, CI/CD integration, and collaborative workflows.

Required Qualifications:

• Master’s or Ph.D. in Statistics, Data Science, Computer Science, Applied Mathematics, or related field.
• 5+ years of experience in Data Science, with at least 2 years in oil & gas, energy, or industrial sectors.
• Hands-on experience with time series forecasting, anomaly detection, and signal processing.
• Proficiency in Python and data science libraries (NumPy, Pandas, scikit-learn, TensorFlow or PyTorch).
• Strong experience with SQL, cloud platforms (AWS, Azure, or GCP), and big data tools (e.g., Spark).
• Experience working in ETL pipelines, cloud data storage, and data lake architectures.

Preferred Qualifications:

• Experience with vCloud environments and cloud-native data processing tools.
• Familiarity with DAX and M query language for Power BI development.
• Prior work with IoT sensor data, SCADA systems, or oilfield production data.
• Knowledge of physics-informed ML or hybrid modeling approaches.
• Experience working on mission-critical, high-complexity industrial analytics projects.