At a Glance
- Tasks: Develop and optimise machine learning models using Spark ML for predictive analytics.
- Company: Synechron is a leading tech firm in financial services, committed to innovation and diversity.
- Benefits: Enjoy hybrid working, swift onboarding, strong market rates, and excellent benefits.
- Why this job: Join a collaborative culture focused on empowerment and cutting-edge technology in machine learning.
- Qualifications: Proficiency in Spark ML, Python, and experience with distributed systems like Hadoop required.
- Other info: Diversity and inclusion are core values; all backgrounds are encouraged to apply.
The predicted salary is between 43200 - 72000 £ per year.
Synechron is looking for a skilled Machine Learning Developer with expertise in Spark ML to work with a leading financial organisation on a global programme of work. The role involves predictive modeling, and deploying training and inference pipelines on distributed systems such as Hadoop. The ideal candidate will design, implement, and optimise machine learning solutions for large-scale data processing and predictive analytics.
Role:
- Develop and implement machine learning models using Spark ML for predictive analytics
- Design and optimise training and inference pipelines for distributed systems (e.g., Hadoop)
- Process and analyse 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
Experience:
- 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
Permanent or Contract position – Swift onboarding – Strong market rates – Excellent benefits – Hybrid working (x3 days in office)
Diversity Statement
Synechron are proud to be an equal opportunity employer. Our Diversity, Equity, and Inclusion (DEI) initiative Same Difference is committed to fostering an inclusive culture promoting equality, diversity and an environment that is respectful to all. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We offer flexible workplace arrangements, mentoring, internal mobility, learning and development programmes to support our global workforce. Empowerment and collaboration are at the core of how we operate.
All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicants gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
Seniority level
- Seniority levelMid-Senior level
Employment type
- Employment typeFull-time
Job function
- Job functionInformation Technology
- IndustriesTechnology, Information and Internet and Financial Services
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Machine Learning Engineer (London) employer: Synechron
Contact Detail:
Synechron Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer (London)
✨Tip Number 1
Familiarise yourself with the latest advancements in Spark ML and distributed systems like Hadoop. This will not only help you understand the technical requirements of the role but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Network with professionals in the machine learning field, especially those who work with financial organisations. Attend meetups or webinars to gain insights and potentially get referrals that could boost your chances of landing the job.
✨Tip Number 3
Prepare to discuss your experience with model evaluation metrics and hyperparameter tuning. Being able to articulate your past successes and challenges in these areas can set you apart from other candidates.
✨Tip Number 4
Showcase your collaborative skills by highlighting any previous experiences where you worked closely with data engineers or cross-functional teams. This is crucial for the role, as collaboration is key to integrating ML workflows with data pipelines.
We think you need these skills to ace Machine Learning Engineer (London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Apache Spark, Spark MLlib, and any relevant predictive modelling techniques. Use specific examples to demonstrate your skills in machine learning and distributed systems.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role at Synechron and explain how your background aligns with their needs. Mention your experience with large-scale data processing and your ability to collaborate with data engineers.
Showcase Relevant Projects: If you have worked on projects involving machine learning models or data processing, include them in your application. Describe your role, the technologies used, and the outcomes achieved to illustrate your capabilities.
Highlight Continuous Learning: Mention any recent courses, certifications, or workshops related to machine learning or distributed computing that you've completed. This shows your commitment to staying updated with advancements in the field.
How to prepare for a job interview at Synechron
✨Showcase Your Spark ML Expertise
Make sure to highlight your experience with Apache Spark and Spark MLlib during the interview. Be prepared to discuss specific projects where you've implemented machine learning models using these technologies, as this will demonstrate your hands-on skills.
✨Discuss Distributed Systems Knowledge
Since the role involves working with distributed systems like Hadoop, be ready to explain your understanding of how these systems work. Share examples of how you've processed large-scale datasets and integrated ML workflows with data pipelines.
✨Prepare for Technical Questions on Model Evaluation
Expect questions about predictive modelling techniques and model evaluation metrics. Brush up on concepts like regression, classification, and clustering, and be ready to discuss how you've fine-tuned hyperparameters to improve model performance.
✨Emphasise Collaboration Skills
Collaboration with data engineers is key in this role. Prepare to talk about your experience working in teams, how you ensure seamless integration of ML solutions, and any challenges you've faced while collaborating on projects.