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- Tasks: Develop optimisation and machine learning models for decision-support software.
- Company: Leading tech firm at Heathrow with a focus on innovation.
- Benefits: Competitive daily rate, flexible working, and opportunities for professional growth.
- Why this job: Join a dynamic team and make an impact in the tech industry.
- Qualifications: Master’s degree or 2+ years of relevant experience in data science or ML.
- Other info: Collaborative environment with a focus on continuous improvement.
The predicted salary is between 36000 - 60000 £ per year.
Location: Heathrow, Waterside, UK
Job Type: Contract – Inside IR35
Rate: 450 GBP Per Day – Inside IR35
Role purpose: This role is responsible for developing industrialized optimisation and machine learning models as part of a full-stack product squad that delivers operations decision-support software.
Skills/capabilities
- Strong knowledge of either machine learning and optimization techniques, including supervised (regression, tree methods, etc.), unsupervised (clustering) learning, and operations research (linear, mixed integer programming, heuristics).
- Fluent in Python (required) and other programming languages (preferred) with strong skills in applying data science, machine learning, and operations research packages (scikit-learn, pandas, numpy, gurobi, etc.) to solve real-life problems and visualise the outcomes (e.g. seaborn).
- Proficient in working with cloud platforms (AWS preferred), code versioning (Git), experiment tracking (e.g. MLflow).
- Experience with cloud-based ML tools (e.g. SageMaker), data and model versioning (e.g. DVC), CI/CD (e.g. GitHub Actions), workflow orchestration (e.g. Airflow/Dagster) and containerised solutions (e.g. Docker, ECS) - nice to have.
- Experience in code testing (unit, integration, end-to-end tests).
- Strong data engineering skills in SQL and Python.
- Proficient in use of Microsoft Office, including advanced Excel and PowerPoint.
Skills
- Advanced analytical skills, including the ability to apply a range of data science and analytic techniques to quickly generate accurate business insights.
- Understanding of the trade-offs of different data science, machine learning, and optimization approaches, and ability to intelligently select which are the best candidates to solve a particular business problem.
- Able to structure business and technical problems, identify trade-offs, and propose solutions.
- Communication of advanced technical concepts to audiences with varying levels of technical skills.
- Managing priorities and timelines to deliver features in a timely manner that meet business requirements.
- Collaborative team-working, giving and receiving feedback, and always seeking to improve team processes.
Qualifications/experience
- Master’s degree or greater in data science, machine learning, or operational research, or 2+ years of highly relevant industry experience (required).
- 0-2 years working on production machine learning or optimization software products at scale (required).
- Experience in developing industrialized software, especially data science or machine learning software products (preferred).
- Experience in relevant business domains (transportation, airlines, operations, network problems) (preferred).
Data Scientist in London employer: Avensys Consulting UK
Contact Detail:
Avensys Consulting UK Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can land you that Data Scientist gig.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects and optimisation models. We recommend using platforms like GitHub to share your code and visualisations – it’s a great way to impress potential employers.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your experience with Python, cloud platforms, and data engineering. We suggest practicing common interview questions and even doing mock interviews with friends.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for talented Data Scientists like you. It’s super easy, and you’ll be one step closer to landing that dream job!
We think you need these skills to ace Data Scientist in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your strong knowledge of machine learning and optimisation techniques in your application. We want to see how you can apply these skills to real-life problems, so don’t hold back on showcasing your experience with Python and relevant packages!
Tailor Your Application: Take a moment to tailor your CV and cover letter to the Data Scientist role. Mention specific projects or experiences that align with our needs, especially those involving cloud platforms and data engineering. This helps us see how you fit into our team!
Be Clear and Concise: When writing your application, keep it clear and concise. We appreciate straightforward communication, so avoid jargon unless it’s necessary. Remember, we want to understand your thought process and how you tackle complex problems.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it makes the whole process smoother for both of us!
How to prepare for a job interview at Avensys Consulting UK
✨Know Your Tech Inside Out
Make sure you’re well-versed in the machine learning and optimisation techniques mentioned in the job description. Brush up on supervised and unsupervised learning methods, and be ready to discuss how you've applied them in real-life scenarios.
✨Showcase Your Python Skills
Since fluency in Python is a must, prepare to demonstrate your coding skills during the interview. You might be asked to solve a problem on the spot, so practice coding challenges and be familiar with libraries like scikit-learn and pandas.
✨Familiarise Yourself with Cloud Platforms
Get comfortable discussing your experience with cloud platforms, especially AWS. Be prepared to talk about any projects where you’ve used tools like SageMaker or Docker, as this will show your practical knowledge of cloud-based ML solutions.
✨Communicate Clearly and Confidently
You’ll need to explain complex technical concepts to non-technical stakeholders. Practice simplifying your explanations and think of examples where you successfully communicated your ideas to a diverse audience.
