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- Tasks: Join a dynamic team to build and maintain data science infrastructure.
- Company: SPG Resourcing values diversity and fosters an inclusive workplace.
- Benefits: Enjoy a competitive salary and opportunities for professional growth.
- Why this job: Work collaboratively on exciting projects that impact real-world applications.
- Qualifications: Proficiency in Python, Databricks, Azure, and strong software engineering skills required.
- Other info: Open to all backgrounds; reasonable accommodations provided throughout the hiring process.
The predicted salary is between 52000 - 78000 £ per year.
This position is for an experienced Machine Learning Engineer to join a newly established data science team. The primary focus is on building and maintaining the infrastructure to support the full data science lifecycle from data ingestion to model deployment, monitoring, and upgrades within Azure and Databricks environments. The engineer will work closely with data scientists in a collaborative, cross-functional setting, helping transition models from research into production.
Key Responsibilities:
- Own and develop deployment frameworks for data science services.
- Ownership of the deployment framework for all data science services.
- Oversight of how data will flow into the data science life cycle from the wider business data warehouse.
- Oversight of the automation of the data science life cycle (dataset build, training, evaluation, deployment, monitoring) when we move to production.
- Automate the data science pipeline (data prep to deployment).
- Collaborate with cross-functional teams to ensure smooth productionisation of models.
- Write clean, production-ready Python code.
- Apply software engineering best practices, CI/CD, TDD.
Required Skills:
- Proficiency in Python, Databricks, and Azure.
- Experience with deployment tools (e.g., AKS, managed endpoints).
- Strong software engineering background (CI/CD, VCS, TDD).
- Ability to integrate ML into business workflows.
Desirable:
- Background in quantitative disciplines (math, stats, physics).
- Experience in finance, insurance, or ecommerce.
- Familiarity with ML frameworks like TensorFlow, XGBoost, and SKLearn.
If this sounds like something you are interested in, please get in contact: thomas.deakin@spgresourcing.com
SPG Resourcing is an equal opportunities employer and is committed to fostering an inclusive workplace which values and benefits from the diversity of the workforce we hire. We offer reasonable accommodation at every stage of the application and interview process.
Machine Learning Engineer (Leeds) employer: SPG Resourcing
Contact Detail:
SPG Resourcing Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (Leeds)
✨Tip Number 1
Familiarise yourself with Azure and Databricks, as these are key platforms for the role. Consider taking online courses or tutorials to enhance your skills and demonstrate your commitment to mastering these tools.
✨Tip Number 2
Showcase your experience with CI/CD and TDD in your discussions. Be prepared to share specific examples of how you've implemented these practices in previous projects, as this will highlight your software engineering background.
✨Tip Number 3
Network with professionals in the data science and machine learning community. Attend meetups or webinars related to machine learning and data engineering to connect with potential colleagues and learn about industry trends.
✨Tip Number 4
Prepare to discuss how you can integrate machine learning into business workflows. Think of examples from your past work where you've successfully collaborated with cross-functional teams to bring models into production.
We think you need these skills to ace Machine Learning Engineer (Leeds)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, Databricks, and Azure. Include specific projects where you've built or maintained deployment frameworks, as well as any relevant automation work you've done in the data science lifecycle.
Craft a Strong Cover Letter: In your cover letter, emphasise your collaborative experience with cross-functional teams and your ability to transition models from research to production. Mention any familiarity with CI/CD and TDD practices, as these are key for the role.
Showcase Relevant Projects: If you have worked on projects involving ML frameworks like TensorFlow, XGBoost, or SKLearn, be sure to include these in your application. Detail your role in these projects and the impact they had on the business.
Proofread Your Application: Before submitting, carefully proofread your application for any errors or typos. A clean, professional presentation can make a significant difference in how your application is perceived.
How to prepare for a job interview at SPG Resourcing
✨Showcase Your Technical Skills
Be prepared to discuss your proficiency in Python, Databricks, and Azure. Bring examples of past projects where you successfully implemented these technologies, especially in deploying machine learning models.
✨Understand the Data Science Lifecycle
Familiarise yourself with the full data science lifecycle, from data ingestion to model deployment. Be ready to explain how you would automate this process and ensure smooth transitions from research to production.
✨Emphasise Collaboration
Highlight your experience working in cross-functional teams. Discuss how you’ve collaborated with data scientists and other stakeholders to achieve common goals, particularly in productionising models.
✨Prepare for Problem-Solving Questions
Expect technical questions that assess your problem-solving skills. Be ready to tackle scenarios related to CI/CD, TDD, and integrating machine learning into business workflows, demonstrating your software engineering background.
