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- Tasks: Develop and optimise machine learning models using Spark ML for predictive analytics.
- Company: Join Synechron, a leader in digital transformation and technology services.
- Benefits: Enjoy flexible work options and a collaborative, innovative culture.
- Why this job: Make an impact with cutting-edge technology while working on large-scale data projects.
- Qualifications: Proficiency in Spark ML, Python, and experience with distributed systems like Hadoop required.
- Other info: Stay ahead in the tech game with continuous learning opportunities.
The predicted salary is between 36000 - 60000 £ per year.
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.
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Proficiency in Apache Spark and Spark MLlib for machine learning tasks.
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Strong understanding of predictive modeling techniques (e.g., regression, classification, clustering).
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Experience with distributed systems like Hadoop for data storage and processing.
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Proficiency in Python, Scala, or Java for ML development.
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Familiarity with data preprocessing techniques and feature engineering.
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Knowledge of model evaluation metrics and techniques.
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Experience with deploying ML models in production environments.
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Understanding of distributed computing concepts and parallel processing.
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ML Engineer employer: Synechron
Contact Detail:
Synechron Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer
✨Tip Number 1
Familiarise yourself with the latest advancements in Spark ML and distributed computing. Join online forums or communities where professionals discuss their experiences and share insights about machine learning frameworks. This will not only enhance your knowledge but also help you network with potential colleagues.
✨Tip Number 2
Work on personal projects that involve building and deploying machine learning models using Spark ML and Hadoop. Showcase these projects on platforms like GitHub to demonstrate your practical skills and understanding of real-world applications, which can set you apart from other candidates.
✨Tip Number 3
Prepare for technical interviews by practising coding challenges related to machine learning algorithms and data processing. Websites like LeetCode or HackerRank can be great resources. Focus on problems that require you to optimise solutions for large datasets, as this is crucial for the role.
✨Tip Number 4
Engage with the machine learning community through meetups or webinars. This can provide you with insights into industry trends and best practices. Additionally, it’s a great way to meet people who might refer you to job openings, including the one at StudySmarter.
We think you need these skills to ace ML Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Spark ML, predictive modeling, and distributed systems like Hadoop. Use specific examples of projects where you've implemented machine learning solutions.
Craft a Compelling Cover Letter: In your cover letter, express your passion for machine learning and how your skills align with the responsibilities listed in the job description. Mention any relevant projects or experiences that demonstrate your expertise.
Showcase Technical Skills: Clearly outline your proficiency in programming languages such as Python, Scala, or Java. Include any relevant certifications or courses that showcase your knowledge of machine learning frameworks and distributed computing.
Highlight Collaboration Experience: Since the role involves collaborating with data engineers, mention any past experiences where you worked in a team setting to integrate ML workflows with data pipelines. This will show your ability to work effectively in a collaborative environment.
How to prepare for a job interview at Synechron
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Spark ML and distributed systems like Hadoop. 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 complex problems, especially in relation to model performance evaluation and hyperparameter tuning.
✨Collaborate Effectively
Since collaboration with data engineers is key, be ready to discuss how you've worked in teams before. Share examples of how you ensured seamless integration of ML workflows with data pipelines.
✨Stay Updated on Industry Trends
Show your passion for machine learning by discussing recent advancements in the field. Mention any new frameworks or technologies you've explored, demonstrating your commitment to continuous learning.
