Machine Learning Engineer

Job Title: Machine Learning Engineer

Location: London (1 days per week onsite) – Flexible

Salary: £45,000 DOE + Benefits

Our Data Analytics business continues to grow and we are now looking for an experienced and technical Machine Learning (ML) Engineer to join one our offices with hybrid or remote UK working. This is an exciting role and would most likely suit someone with previous experience in a similar role where they have gained knowledge and experience of designing, building, optimising, deploying and managing business-critical machine learning models using Azure ML in Production environments. You must have good technical knowledge of Phyton, SQL, CI/CD and familiar with Power BI.

A FTSE 250 company, they combine expertise and insight with advanced technology and analytics to address the needs of over 1,400 schemes and their sponsoring employers on an ongoing and project basis. We undertake administration for over one million members and provide advisory services to schemes and corporate sponsors in respect of schemes of all sizes, including 88 with assets over £1bn. We also provide wider ranging support to insurance companies in the life and bulk annuities sector.

The Team

The client is a specialist and multi-disciplinary team consisting of actuaries, data scientist and developers. Our 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.

The Role

As a Machine Learning Engineer you will:

• Model development. 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.
• Machine Learning Operations. 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-scare data efficiently. Implement and maintain monitoring of model drifts, data-quality alerts, scheduled r-training pipelines.
• Data Management and Preprocessing. Collect, clean and preprocess large datasets to facilitate analysis and model training. Implement data pipelines and ETL processes to ensure data availability and quality.
• Software Development. Write clean, efficient and scalable code in Python. Utilize CI/CD practices for version control, testing and code review.
• Work closely with actuarial analysts, actuarial modelling team (AMT) and other colleagues to integrate data science findings into practical advice and strategies.
• Stay abreast of new trends and technologies in Data Science technologies and pensions to identify opportunities for innovation.
• Provide training and support to other team members on using machine learning tools and understanding analytical techniques.
• Interpret and explain machine learning concepts and findings to other members of the analytics team and non-technical stakeholders.

Your profile

Essential Criteria

• Previous 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.
• Good written and verbal communication and interpersonal skills. Ability to convey technical concepts to non-technical stakeholders.
• Experience in the pensions or similar regulated financial services industry is highly desirable.
• Experience in working within a multidisciplinary team would be beneficial.

We offer an attractive reward package; typical benefits can include:

• Competitive salary
• Participation in annual discretionary Bonus Scheme
• 25 days holiday plus flexibility to buy or sell holiday
• Flexible Bank holidays
• Pension scheme, matching contribution structure
• Healthcare cash plan
• Flexible Benefits Scheme to support you in and out of work, helping you look after you and your family covering Security & Protection, Health & Wellbeing, Lifestyle
• Life Assurance cover, four times basic salary
• Rewards (offers High Street discounts and savings from retailers and services providers as well as offers available via phone)
• Employee Assistance Programme for you and your household
• Access to a digital GP service
• Paid volunteering day when participating in Company organised events
• Staff referral scheme when you introduce a friend