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- Tasks: Lead the design and management of MLOps pipelines for machine learning models.
- Company: Join a well-established insurance firm embracing AI to revolutionise their industry.
- Benefits: Enjoy flexible working, a generous learning fund, and 27 days annual leave.
- Why this job: Be part of a transformative journey in AI with a supportive and innovative team culture.
- Qualifications: Strong cloud platform experience and knowledge of CI/CD tools required.
- Other info: Opportunity for continuous learning and professional development on company time.
The predicted salary is between 75000 - 90000 £ per year.
My client works in the Insurance / Risk Management space and is relatively well established, having served their clients over the last 12 years. The firm was a relatively late adopter of AI, mostly due to some of the red tape and regulations affiliated with their more traditional sector. However, with a new CEO onboard and a more pragmatic approach, the firm are keen to play catch-up and help revolutionise their industry as others are doing.
To help accelerate this journey, they’ve invested heavily in the AI team and have now got some heavy-hitters in to lead on some cool, transformational projects. With a few MLEs already hired, they’re now looking for a senior MLOps individual to spearhead cloud deployment and management of some of the Key ML pipelines/infrastructure.
Day-to-Day Responsibilities:
- Design, implement, and maintain robust MLOps pipelines to ensure seamless deployment, monitoring, and scaling of machine learning models in production.
- Collaborate within the team to operationalise models, ensuring they are scalable, reliable, and efficient.
- Develop and maintain CI/CD pipelines for ML workflows, integrating automated testing, model validation, and version control.
- Monitor model performance in production, identifying and resolving issues such as data drift, model degradation, and latency bottlenecks.
- Optimise cloud infrastructure for machine learning workloads, ensuring cost-efficiency and scalability.
- Document processes, workflows, and best practices to ensure knowledge sharing and continuity within the team.
It goes without saying, but given the novelty of MLOps roles on the whole, the engineer should be keen on keeping up with best practices, attending workshops/events (on company time) and ensuring that they stay at the top of their game.
Technical Expertise:
- Strong experience with cloud platforms such as AWS or Azure, including services like SageMaker, MLflow/Kubeflow.
- Docker/Kubernetes
- Solid understanding of CI/CD tools (Jenkins, GitLab CI, GitHub Actions) and version control systems (aka Git).
- Experience with IAC - Terraform or CloudFormation.
Nice to haves:
- Familiarity with data engineering tools/frameworks (Apache Spark/Airflow) for pre-processing and managing large datasets.
- Experience of working within the Insurance/Risk sector really beneficial but not essential.
Benefits:
- Good allowance for continued learning/development – bolstered by a £2,200 individual yearly learning fund.
- Flexible working to suit care/caregiving needs.
- Cycle to work schemes/season ticket initiatives.
- 27 days of annual leave rising to 30 after 3 years of service.
- Salary between £75,000 - £90,000 DOE with yearly review (financial year).
Data Science Manager (Remote) employer: NearTech Search
Contact Detail:
NearTech Search Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Science Manager (Remote)
✨Tip Number 1
Familiarise yourself with the specific cloud platforms mentioned in the job description, like AWS or Azure. Having hands-on experience with services such as SageMaker or MLflow can set you apart from other candidates.
✨Tip Number 2
Engage with the MLOps community by attending workshops and events. This not only helps you stay updated on best practices but also allows you to network with professionals who might have insights into the company or role.
✨Tip Number 3
Showcase your understanding of CI/CD tools and version control systems during any discussions or interviews. Being able to discuss your experience with tools like Jenkins or GitLab CI will demonstrate your technical expertise.
✨Tip Number 4
If you have any experience in the Insurance or Risk Management sector, make sure to highlight it. Even if it's not essential, it can give you an edge over other applicants who may not have that background.
We think you need these skills to ace Data Science Manager (Remote)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in MLOps, cloud platforms like AWS or Azure, and any specific tools mentioned in the job description. Use keywords from the job listing to ensure your application stands out.
Craft a Compelling Cover Letter: Write a cover letter that not only showcases your technical expertise but also demonstrates your enthusiasm for the role and the company's mission. Mention how your background aligns with their goals of revolutionising the insurance industry through AI.
Showcase Relevant Projects: If you have worked on projects involving MLOps pipelines, cloud deployment, or CI/CD workflows, be sure to include these in your application. Provide specific examples of your contributions and the impact they had on the project.
Highlight Continuous Learning: Given the emphasis on staying updated with best practices, mention any workshops, courses, or certifications you have completed related to MLOps or machine learning. This shows your commitment to professional development and aligns with the company's values.
How to prepare for a job interview at NearTech Search
✨Showcase Your MLOps Knowledge
Make sure to highlight your experience with MLOps pipelines and cloud platforms like AWS or Azure. Be prepared to discuss specific projects where you've designed, implemented, or maintained these systems.
✨Demonstrate Collaboration Skills
Since the role involves working closely with a team, share examples of how you've successfully collaborated on projects. Emphasise your ability to operationalise models and ensure they are scalable and reliable.
✨Discuss CI/CD Experience
Be ready to talk about your experience with CI/CD tools such as Jenkins or GitLab CI. Explain how you've integrated automated testing and model validation into your workflows to enhance efficiency.
✨Stay Updated on Best Practices
Express your commitment to continuous learning in the MLOps field. Mention any workshops or events you've attended recently and how you keep up with industry trends to stay at the top of your game.
