Lead Machine Learning Engineer - Databricks experience

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Lead Machine Learning Engineer - Databricks experience, birmingham

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Client:

ON Data Staffing

Location:

birmingham, United Kingdom

Job Category:

Other

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EU work permit required:

Yes

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Job Views:

3

Posted:

26.08.2025

Expiry Date:

10.10.2025

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Job Description:

ON Data Staffing is currently seeking a talented Machine Learning Engineer with expertise in Databricks to join our client's remote team in the United Kingdom.

As a key member of our client's Data Science and Machine Learning team, the Databricks ML Engineer will play a crucial role in developing and implementing state-of-the-art machine learning models and algorithms to solve complex business problems. This is an exciting opportunity to work with cutting-edge technologies and make a significant impact in a fast-paced, collaborative environment.

Responsibilities:

• Collaborate with cross-functional teams to understand business requirements and design scalable machine learning solutions.
• Develop and deploy machine learning models using Databricks ML and other relevant technologies.
• Conduct thorough analysis of large-scale datasets to extract meaningful insights and drive decision-making.
• Optimize and fine-tune machine learning models for performance and accuracy.
• Stay current with the latest advancements in machine learning and data science to continuously improve our capabilities.

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field.
• Proven experience as a Machine Learning Engineer or Data Scientist, with a focus on building and deploying machine learning models.
• Strong proficiency in Databricks ML and other relevant tools such as TensorFlow, PyTorch, or scikit-learn.
• Solid understanding of machine learning algorithms, statistical modeling, and data preprocessing techniques.
• Experience working with big data technologies such as Apache Spark is a plus.
• Excellent communication and collaboration skills, with the ability to work effectively in a remote team environment.

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