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- Tasks: Implement machine learning models and manage data pipelines using Azure technologies.
- Company: Join a forward-thinking company focused on innovative data solutions.
- Benefits: Enjoy flexible working options and opportunities for professional growth.
- Why this job: Be part of a dynamic team driving impactful data projects in a collaborative environment.
- Qualifications: 10+ years of experience in data science with strong Python and Azure skills required.
- Other info: Stay updated with the latest industry trends while enhancing your technical expertise.
The predicted salary is between 57600 - 84000 £ per year.
Experience: 10+ years
Must have good experience in implementing machine learning models such as Prophet, ARIMA, SARIMA, XGBoost, ElasticNet, Ridge, Lasso, Random Forest, and Linear Regression on time-series data.
Proficient in using Python ML packages such as scikit-learn, sktime, and darts.
Must have strong expertise in key techniques for time series feature engineering, including lag features, rolling window statistics, Fourier transforms, and handling seasonality.
Proven ability to tune the performance of existing deployed forecasting models.
Must have experience with Azure Machine Learning Python SDK v1/v2 to:
- Manage data, models, and environments
- Build/debug AML pipelines to stitch together multiple tasks (feature engineering, training, registering models, etc.) and production workflows using Azure ML pipelines
- Schedule Azure ML jobs
- Deploy registered models to create endpoints.
Good to have experience with K-Means clustering.
Must have utilized Azure services such as Azure Data Factory, Azure Databricks, Azure Data Lake, and Azure Key Vault to architect and maintain scalable data solutions.
Design, develop, and deploy new Azure Data Factory (ADF) pipelines for data ingestion, transformation, and logging, ensuring robustness and reliability.
Proficiently transform and manipulate data using PySpark and Python, leveraging their capabilities to derive actionable insights from complex datasets.
Collaborate with cross-functional teams to understand data requirements and translate them into effective technical solutions.
Lead the implementation and optimization of CI/CD pipelines using Azure DevOps, ensuring a seamless build and release flow for data infrastructure and applications.
Drive best practices in data engineering, including data governance, security, and performance optimization.
Stay abreast of industry trends and emerging technologies, contributing to the continuous improvement of our data engineering capabilities.
Data Scientist - ETRM employer: Vallum Associates
Contact Detail:
Vallum Associates Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - ETRM
✨Tip Number 1
Network with professionals in the data science field, especially those who have experience with Azure Machine Learning. Attend meetups or webinars to connect with potential colleagues and learn about their experiences at companies like ours.
✨Tip Number 2
Showcase your practical experience with machine learning models by working on personal projects or contributing to open-source projects. This hands-on experience can be a great conversation starter during interviews.
✨Tip Number 3
Familiarise yourself with the latest trends in time series analysis and Azure services. Being able to discuss recent advancements or tools can demonstrate your passion and commitment to the field.
✨Tip Number 4
Prepare for technical interviews by practising coding challenges related to Python and machine learning. Websites like LeetCode or HackerRank can help you sharpen your skills and boost your confidence.
We think you need these skills to ace Data Scientist - ETRM
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning models and Python ML packages. Include specific examples of projects where you've implemented techniques like ARIMA or Random Forest, and mention your proficiency with Azure services.
Craft a Strong Cover Letter: In your cover letter, express your passion for data science and how your 10+ years of experience align with the job requirements. Discuss your expertise in time series feature engineering and your ability to tune forecasting models, showcasing your problem-solving skills.
Showcase Relevant Projects: If you have worked on relevant projects, summarise them in your application. Highlight your experience with Azure Machine Learning and any CI/CD pipelines you've implemented, as well as your collaboration with cross-functional teams.
Proofread Your Application: Before submitting, carefully proofread your application for any spelling or grammatical errors. Ensure that all technical terms are used correctly and that your application is clear and concise, reflecting your attention to detail.
How to prepare for a job interview at Vallum Associates
✨Showcase Your Machine Learning Expertise
Be prepared to discuss your experience with various machine learning models like Prophet, ARIMA, and XGBoost. Highlight specific projects where you implemented these models on time-series data, and be ready to explain the outcomes and any challenges you faced.
✨Demonstrate Your Python Proficiency
Since the role requires strong skills in Python ML packages, ensure you can talk about your experience with scikit-learn, sktime, and darts. Consider preparing a brief coding example or discussing a project where you effectively used these tools.
✨Discuss Azure Machine Learning Experience
Familiarise yourself with Azure Machine Learning SDK and be ready to discuss how you've managed data, built pipelines, and deployed models. Sharing specific examples of how you’ve used Azure services will show your hands-on experience and understanding of the platform.
✨Highlight Collaboration Skills
This role involves working with cross-functional teams, so be prepared to share examples of how you've collaborated with others to meet data requirements. Discuss how you translated complex data needs into technical solutions, showcasing your communication and teamwork abilities.
