At a Glance
- Tasks: Design and build scalable data pipelines for machine learning solutions.
- Company: Rapidly growing financial services business with a focus on innovation.
- Benefits: Hybrid working, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with exposure to cutting-edge technologies.
- Why this job: Join a high-calibre team and shape the future of machine learning.
- Qualifications: Strong background in software, data, or machine learning engineering.
The predicted salary is between 60000 - 80000 £ per year.
London - Hybrid working
We are partnering with a rapidly growing financial services business that is investing heavily in its machine learning and data platform capabilities. This is a hands-on engineering role focused on helping Data Science teams build, deploy, and scale machine learning solutions in production. The work sits at the intersection of data engineering, machine learning engineering, and MLOps, with a strong focus on production systems, feature engineering, workflow orchestration, and model lifecycle management.
What you'll be doing:
- Design and build scalable data and feature engineering pipelines to support machine learning workloads
- Work closely with Data Scientists to operationalise models and improve the end-to-end machine learning lifecycle
- Develop and optimise PySpark-based data processing workflows and training pipelines
- Build and maintain workflow orchestration frameworks using tools such as Airflow, Databricks Workflows, or similar technologies
- Support model training, deployment, monitoring, and experiment tracking in production environments
- Contribute to feature engineering, feature management, and model performance optimisation initiatives
- Help establish engineering standards, platform capabilities, and best practices across ML and data workflows
- Collaborate with engineering and product teams to deliver reliable, production-grade machine learning systems
What we're looking for:
- Strong software, data, or machine learning engineering background
- Experience building production data and machine learning systems at scale
- Experience with Spark or PySpark in production environments
- Experience supporting machine learning workflows, training pipelines, or feature engineering processes
- Exposure to technologies such as Databricks, SageMaker, Azure ML, Vertex AI, MLflow, Airflow, feature stores, or similar platforms
- Experience working closely with Data Scientists to deploy and operate machine learning solutions
- Strong understanding of cloud-based engineering environments (AWS, Azure, or GCP)
Nice to have:
- Experience with recommendation systems, forecasting, optimisation, or other large-scale machine learning workloads
- Experience with Kubernetes, Terraform, or cloud infrastructure automation
- Exposure to modern AI/LLM workflows and MLOps practices
This is an opportunity to join a high-calibre engineering team and play a key role in shaping how machine learning is delivered and operated at scale.
Senior Data & ML Engineer employer: DW Search
Join a dynamic and rapidly growing financial services business in London, where innovation meets opportunity. As a Senior Data & ML Engineer, you'll be part of a high-calibre engineering team that fosters a collaborative work culture, prioritises employee growth, and offers hybrid working arrangements. With a strong focus on machine learning and data platform capabilities, this role provides unique advantages such as hands-on experience with cutting-edge technologies and the chance to shape the future of machine learning solutions in production.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data & ML Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like DW Search!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Data & ML Engineer at DW Search.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like DW Search.
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We think you need these skills to ace Senior Data & ML Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at DW Search, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at DW Search. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at DW Search
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at DW Search!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
