At a Glance
- Tasks: Join a dynamic team to develop machine learning models for predicting pension-related outcomes.
- Company: Be part of an award-winning company recognised for its innovative actuarial software.
- Benefits: Enjoy remote or hybrid work options and a competitive salary of £40,000 - £45,000.
- Why this job: Make a real impact in the pensions sector while working with cutting-edge technology.
- Qualifications: Experience with Azure ML, Python, SQL, and CI/CD practices is essential.
- Other info: This role offers a chance to collaborate with experts and enhance your skills in a growing field.
The predicted salary is between 34000 - 45000 £ per year.
We are on the lookout for an experienced Machine Learning Operations Engineer (ML Ops Engineer) to join this growing team who have been awarded Actuarial Software of the Year. The role in this mission is to pioneer advancements in the field of pensions and beyond, leveraging state-of-the-art technology to extract valuable and timely insights from data. This enables the consultant to better advise Trustees and Corporate clients on a wide range of actuarial-related areas.
ML Ops Engineer Main Duties:
- Work collaboratively with actuarial analysts to develop machine learning and statistical models to predict outcomes related to pension schemes, such as life expectancy, default risk, or investment returns.
- Identify appropriate machine learning algorithms and apply them to enhance predictions, automate decision-making processes, and improve client offerings.
- Responsible for designing, deploying, maintaining and refining statistical and machine learning models using Azure ML.
- Optimize model performance and computational efficiency.
- Ensure that applications run smoothly and handle large-scale data efficiently.
- Implement and maintain monitoring of model drifts, data-quality alerts, scheduled retraining pipelines.
- Collect, clean and preprocess large datasets to facilitate analysis and model training.
- Implement data pipelines and ETL processes to ensure data availability and quality.
- Write clean, efficient and scalable code in Python.
- Utilize CI/CD practices for version control, testing and code review.
ML Ops Engineer Skills Required:
- Experience in designing, building, optimising, deploying and managing business-critical machine learning models using Azure ML in Production environments.
- Experience in data wrangling using Python, SQL and ADF.
- Experience in CI/CD and DevOps/MLOps and version control.
- Familiarity with data visualization and reporting tools, ideally PowerBI.
- Experience in the pensions or similar regulated financial services industry.
Due to the volume of applications received for positions, it will not be possible to respond to all applications and only applicants who are considered suitable for interview will be contacted.
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ML Operations Engineer employer: Proactive Appointments
Contact Detail:
Proactive Appointments Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Operations Engineer
✨Tip Number 1
Familiarise yourself with Azure ML and its capabilities. Since the role specifically requires experience in deploying and managing machine learning models using Azure, having hands-on experience or relevant projects to discuss can set you apart during interviews.
✨Tip Number 2
Brush up on your Python skills, especially in data wrangling and writing clean, efficient code. Being able to demonstrate your coding abilities through practical examples or past projects will show that you're ready for the technical challenges of the role.
✨Tip Number 3
Understand the specific challenges faced in the pensions industry. Research current trends and issues related to actuarial science and how machine learning can provide solutions. This knowledge will help you engage more effectively during interviews and discussions.
✨Tip Number 4
Network with professionals in the field of ML Ops and actuarial science. Attend relevant meetups or webinars to connect with others in the industry. This can lead to valuable insights and potentially even referrals for the position at StudySmarter.
We think you need these skills to ace ML Operations Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning operations, particularly with Azure ML. Emphasise your skills in data wrangling, Python, and CI/CD practices to align with the job requirements.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for machine learning and your understanding of the pensions industry. Mention specific projects or experiences that demonstrate your ability to design and deploy machine learning models.
Highlight Relevant Skills: In your application, clearly outline your experience with data pipelines, ETL processes, and model monitoring. Use specific examples to illustrate how you've optimised model performance and handled large datasets.
Proofread Your Application: Before submitting, carefully proofread your application for any spelling or grammatical errors. A polished application reflects your attention to detail, which is crucial for an ML Ops Engineer role.
How to prepare for a job interview at Proactive Appointments
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Azure ML and how you've designed, deployed, and optimised machine learning models in production. Bring examples of your work that demonstrate your technical prowess, especially in Python and SQL.
✨Understand the Business Context
Familiarise yourself with the pensions industry and the specific challenges it faces. This will help you articulate how your skills can directly benefit the company and its clients, particularly in areas like life expectancy and investment returns.
✨Demonstrate Collaboration
Since the role involves working closely with actuarial analysts, be ready to discuss past experiences where you've collaborated effectively in a team. Highlight your communication skills and how you’ve contributed to joint projects.
✨Prepare for Problem-Solving Questions
Expect to face questions that assess your problem-solving abilities, particularly in data wrangling and model optimisation. Practice articulating your thought process and the steps you would take to tackle complex data-related challenges.