Data Scientist ( Energy sector Experience )

Role Title: ETRM Data Scientist

Experience: 7+ years

B2B Contract

Must have skills: Time-series forecasting, Python-based MLOps, PySpark Proficiency, Azure Expertise (ADF, Data bricks, Data lake)

Good to have: Energy sector experience

Job Description:

• Education Requirements:
• Master’s degree in mathematics, Statistics, Data Science, or related fields is mandatory.
• A Ph.D. in Mathematics, Statistics, Data Science, or similar areas is preferred but not mandatory.

• Mandatory skills:
• Data Science:
• Extensive experience in time-series forecasting, predictive modelling, and deep learning.
• Proficient in designing reusable and scalable machine learning systems.
• Proficiency in implementing techniques such as ARIMA, LSTM, Prophet, Linear Regression, and Random Forest to ensure accurate forecasting and insights.
• Strong command of machine learning libraries, including scikit-learn, XGBoost, Darts, TensorFlow, and PyTorch, along with data manipulation tools like Pandas and NumPy.
• Proven expertise in designing and implementing explicit ensemble techniques such as stacking, boosting and bagging to improve model accuracy and robustness.
• Proven track record of analysing and optimizing performance of operational machine learning models to ensure long-term efficiency and reliability.
• Expertise in retraining and fine-tuning models based on evolving data trends and business requirements.
• MLOps Implementation:
• Proficiency in leveraging Python-based MLOps frameworks for automating machine learning pipelines, including model deployment, monitoring, and periodic retraining.
• Advanced experience in using the Azure Machine Learning Python SDK to design and implement parallel model training workflows, incorporating distributed computing, parallel job execution, and efficient handling of large-scale datasets in managed cloud environments.
• PySpark Proficiency
• Strong experience in PySpark for scalable data processing and analytics.

• Azure Expertise:
• Azure Machine Learning: Managing parallel model training, deployment, and operationalization using the Python SDK.
• Azure Databricks: Collaborating on data engineering and analytics tasks using PySpark/Python.
• Azure Data Lake: Implementing scalable storage and processing solutions for large datasets.

• Preferred skills:
• K-Means Clustering: Experience in applying k-means clustering for data segmentation and pattern identification.
• Bottom-Up Forecasting: Skilled in creating granular bottom-up forecasting models for hierarchical insights.
• Azure Data Factory : Designing, orchestrating, and managing pipelines for seamless data integration and processing.
• knowledge of power trading concepts.
• Generative AI (GenAI): Experience in applying generative AI models, such as GPT or similar frameworks.