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- Tasks: Develop and optimise machine learning models using Spark ML for predictive analytics.
- Company: Join a forward-thinking tech company at the forefront of machine learning innovation.
- Benefits: Enjoy a hybrid work model with flexible remote options and competitive pay.
- Why this job: Be part of a dynamic team, working on cutting-edge technology that impacts real-world applications.
- Qualifications: Proficiency in Spark ML, Python, and experience with distributed systems like Hadoop required.
- Other info: This is a long-term contract role based in London, offering £550-600 per day.
The predicted salary is between 48000 - 72000 £ per year.
Join to apply for the Machine Learning Engineer role at Synechron
Join to apply for the Machine Learning Engineer role at Synechron
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Direct message the job poster from Synechron
Synechron are seeking a skilled Machine Learning Developer with expertise in Spark ML, predictive modeling, and deploying training and inference pipelines on distributed systems such as Hadoop. The ideal candidate will design, implement, and optimize machine learning solutions for large-scale data processing and predictive analytics.
Responsibilities
Develop and implement machine learning models using Spark ML for predictive analytics.
Design and optimize training and inference pipelines for distributed systems (e.g., Hadoop).
Process and analyze large-scale datasets to extract meaningful insights and features.
Collaborate with data engineers to ensure seamless integration of ML workflows with data pipelines.
Evaluate model performance and fine-tune hyperparameters to improve accuracy and efficiency.
Implement scalable solutions for real-time and batch inference.
Monitor and troubleshoot deployed models to ensure reliability and performance.
Stay updated with advancements in machine learning frameworks and distributed computing technologies.
Requirements:
- Proficiency in Apache Spark and Spark MLlib for machine learning tasks.
- Strong understanding of predictive modeling techniques (e.g., regression, classification, clustering).
- Experience with distributed systems like Hadoop for data storage and processing.
- Proficiency in Python, Scala, or Java for ML development.
- Familiarity with data preprocessing techniques and feature engineering.
- Knowledge of model evaluation metrics and techniques.
- Experience with deploying ML models in production environments.
- Understanding of distributed computing concepts and parallel processing.
Seniority level
-
Seniority level
Not Applicable
Employment type
-
Employment type
Contract
Job function
-
Job function
Consulting
-
Industries
Technology, Information and Internet and Financial Services
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Machine Learning Engineer employer: Synechron
Contact Detail:
Synechron Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Make sure to showcase your hands-on experience with Apache Spark and Spark ML in any discussions or networking opportunities. Highlight specific projects where you've successfully implemented machine learning models, as this will demonstrate your practical skills to potential employers.
✨Tip Number 2
Engage with the machine learning community by attending meetups or webinars focused on distributed systems and predictive analytics. This not only helps you stay updated with the latest trends but also allows you to connect with professionals who might refer you to job openings at companies like us.
✨Tip Number 3
When networking, be prepared to discuss your experience with model evaluation metrics and hyperparameter tuning. Employers are keen to know how you ensure the accuracy and efficiency of your models, so having concrete examples ready can set you apart from other candidates.
✨Tip Number 4
Stay proactive in learning about the latest advancements in machine learning frameworks and distributed computing technologies. Consider contributing to open-source projects or writing articles on platforms like GitHub to showcase your knowledge and passion for the field, which can catch our attention when we review applications.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Apache Spark, Spark MLlib, and any relevant predictive modeling techniques. Use specific examples of projects where you've implemented machine learning models or worked with distributed systems like Hadoop.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss how your skills in Python, Scala, or Java align with the job requirements, and mention any successful projects that demonstrate your ability to design and optimize machine learning solutions.
Showcase Relevant Projects: If you have a portfolio or GitHub repository, include links to projects that showcase your expertise in machine learning and distributed systems. Highlight any work involving real-time and batch inference, as well as model evaluation and tuning.
Proofread and Edit: Before submitting your application, carefully proofread your documents for any spelling or grammatical errors. Ensure that your technical terminology is accurate and that your application presents a professional image.
How to prepare for a job interview at Synechron
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Apache Spark and Spark MLlib in detail. Highlight specific projects where you've implemented machine learning models, focusing on the techniques you used and the outcomes achieved.
✨Demonstrate Problem-Solving Abilities
Expect to face technical challenges during the interview. Practice explaining your thought process when tackling problems related to predictive modeling and distributed systems, as this will showcase your analytical skills.
✨Discuss Collaboration Experience
Since collaboration with data engineers is crucial, be ready to share examples of how you've worked in teams. Emphasise your ability to integrate ML workflows with data pipelines and how you’ve communicated complex ideas effectively.
✨Stay Updated on Industry Trends
Research the latest advancements in machine learning frameworks and distributed computing technologies. Being knowledgeable about current trends will demonstrate your passion for the field and your commitment to continuous learning.
