At a Glance
- Tasks: Design and implement cutting-edge data science and machine learning solutions.
- Company: Join a forward-thinking tech group focused on innovation and collaboration.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Dynamic role with a focus on cloud technology and data-driven decision making.
- Why this job: Lead a talented team and make a real impact in the tech industry.
- Qualifications: 5+ years in data science or machine learning with leadership experience.
The predicted salary is between 80000 - 100000 £ per year.
About the Role
As a Lead Data Science Engineer, you will be designing, developing and implementing data science & machine learning solutions to tackle large and small technology problems across the Group. Your role involves hands‑on development through the entire data science life cycle, from academic and industry research to crafting operational model pipelines. This will be complemented by leading a small, highly skilled team with whom you will collaborate, teach, learn and ensure they are an industry leading example of what data science can achieve. You’ll be part of a growing team of Data Science Engineers in a highly influential role with considerable executive level focus as the Group matures its software engineering and data led approach. You will engage directly with customers, prioritise multiple requests, and educate business areas on how to use data throughout the decision‑making process. You’ll employ machine learning and data science techniques to transform areas from delivering key engineering projects to optimising new cloud platforms and enhancing group security.
Responsibilities
- Responding to enterprise project requests for data science and machine learning expertise.
- Wrangling data from various sources (system and colleague activity log data stored across cloud and on‑premise platforms).
- Researching and developing predictive models using approaches new to the organisation and implementing into dedicated MLOps pipelines that ensure infrastructure choices are secure, resilient, efficient and fast.
- Developing new analytics and models that help us truly understand our colleague experience to improve the engineering choices we make.
- Building capability to predict when and what incidents are likely to happen so that root causes are addressed before security or infrastructure is impacted.
- Being a hands‑on researcher, designer and developer of industry leading techniques.
Experience
- Proven practice as an expert and established Data Scientist or Machine Learning Engineer for a minimum of 5 years.
- Leadership experience to lead, develop and support a multi‑site technical team.
- Experience interacting with the technical and engineering community both internally and externally.
- Developed communication skills, adept at conversing with technical/non‑technical audiences and influencing widely.
- Code using Python, SQL or similar languages and familiarity with modern data technologies such as Apache Spark, Hadoop and model workflow tools like MLFlow.
- Experience of developing and implementing data science or machine learning into Cloud platforms with specific exposure to Azure Databricks and Google Vertex AI being useful.
- Hands‑on experience of feature engineering, development, validation and implementation as applied to sophisticated data science and modelling techniques (such as density‑based clustering techniques, support vector machines, auto‑encoders, multi‑class clustering models and convolutional neural networks).
- Experience of applying machine learning to cyber‑security or other domains using monitoring and alerting in data streams is advantageous.
- Ability to work in an agile environment, interacting with scrum masters and authoritatively utilising tools such as Jira, GitHub, GitHub Project and GIT.
- A validated understanding of data engineering for data science and machine learning.
- Familiarity with graph/NoSQL databases, relational data bases, data streaming and how these can be used to optimise model performance.
- An active curiosity about the ongoing development of data science techniques, cloud capability and technology.
Qualifications
- Masters or PhD in mathematics, applied science, computer science or related quantitative field.
- Demonstrable years' experience within data science and/or machine learning in a commercial, not‑for‑profit or research setting.
Lead Data Science Engineer- Data Science & Machine Learning employer: Lloyds Banking Group
As a Lead Data Science Engineer at our company, you will thrive in a dynamic and innovative environment that prioritises employee growth and collaboration. We offer competitive benefits, a supportive work culture, and the opportunity to lead a talented team while working on cutting-edge data science and machine learning projects that directly impact our organisation's success. Located in a vibrant area, our workplace fosters creativity and engagement, making it an excellent choice for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Science Engineer- Data Science & Machine Learning
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the data science community. Attend meetups, webinars, or conferences where you can chat with industry leaders and potential colleagues. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving machine learning and data science techniques. Share it on platforms like GitHub or your personal website, and don’t forget to link it in your applications. It’s a great way to demonstrate your hands-on experience.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms, as you’ll need to communicate effectively with both technical and non-technical audiences. Mock interviews can be super helpful here!
✨Tip Number 4
Don’t just apply anywhere; apply through our website! We’re always on the lookout for talented individuals like you. Tailor your application to highlight your leadership experience and technical expertise, and let us know how you can contribute to our team!
We think you need these skills to ace Lead Data Science Engineer- Data Science & Machine Learning
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Lead Data Science Engineer role. Highlight your hands-on development experience and leadership skills, as these are key for us.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about data science and machine learning. Share specific examples of projects you've led or contributed to, especially those that align with our mission.
Showcase Your Technical Skills:Don’t forget to mention your proficiency in Python, SQL, and any relevant data technologies. We want to see how you’ve applied these skills in real-world scenarios, so be specific!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity!
How to prepare for a job interview at Lloyds Banking Group
✨Know Your Data Science Fundamentals
Brush up on your core data science concepts and techniques. Be ready to discuss your experience with predictive models, feature engineering, and the specific machine learning algorithms mentioned in the job description. This will show that you not only understand the theory but can also apply it practically.
✨Showcase Your Leadership Skills
Since this role involves leading a team, be prepared to share examples of how you've successfully managed and developed teams in the past. Highlight your ability to mentor others and foster collaboration, as well as any experiences where you influenced decision-making within a technical environment.
✨Demonstrate Your Technical Proficiency
Familiarise yourself with the tools and technologies listed in the job description, such as Python, SQL, Azure Databricks, and MLFlow. Be ready to discuss specific projects where you've used these technologies, and consider preparing a brief demo or code snippet to showcase your skills.
✨Engage with Real-World Applications
Prepare to discuss how you've applied data science and machine learning to solve real-world problems, particularly in areas like cyber-security or cloud platforms. Think of specific examples where your work had a measurable impact, and be ready to explain your thought process and the outcomes.
