Senior Data Scientist (Oslo)

at  WorkNest Technologies LTD

DESCRIPTION

We are seeking an experienced Data Scientist to join our team and work on developing the next generation of anomaly detection models for hydro power energy production systems. The ideal candidate will experiment with complex machine learning (ML) and artificial intelligence (AI) techniques to outperform our current basic statistical models. This role requires a deep understanding of big data processing, time series data, and ML model development, with a specific focus on hydro power systems. We already have good data ingestion- and transformation pipelines in place, meaning that the main focus will be on improving the anomaly detection model.

REQUIREMENTS

• Proficiency with Azure Databricks and PySpark;
• Strong programming skills in Python and familiarity with relevant libraries such as scikit-learn, TensorFlow;
• Experience with anomaly detection models;
• Advanced Analytical Thinking;
• Proficiency in ML/AI Techniques and experience with MLflow;
• Familiarity with data management tools and platforms;
• Knowledge of agile development methodologies;
• Strong problem-solving skills and attention to detail;
• Excellent communication and teamwork abilities.

• Develop and implement advanced ML/AI models for anomaly detection in hydro power energy production systems;
• Experiment with and evaluate different modeling techniques to improve anomaly detection accuracy over current statistical models;
• Design, train, and test models using big data and time series data;
• Utilize Azure Databricks and PySpark for processing and analyzing large datasets;
• Optimize data pipelines for efficient model training and inference;
• Work closely with data engineers, domain experts, and other stakeholders to gather requirements and ensure models meet business needs;
• Communicate findings and model performance metrics to technical and non-technical stakeholders;
• Document model development processes, data workflows, and findings;
• Generate detailed reports and presentations to share insights and model evaluations.