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For Companies At a Glance
- Tasks: Use machine learning and optimisation techniques to solve real-world problems and visualise outcomes.
- Company: Join a forward-thinking company that values innovation and collaboration.
- Benefits: Enjoy competitive pay, health perks, remote work options, and growth opportunities.
- Why this job: Make an impact with your data skills while working on exciting projects.
- Qualifications: Master’s degree in data science or 2+ years of relevant experience required.
- Other info: Dynamic team environment with opportunities for career advancement.
The predicted salary is between 28800 - 48000 £ per year.
Responsibilities
- 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) is nice to have.
- Experience in code testing (unit, integration, end-to-end tests).
- Strong data engineering skills in SQL and Python.
- Proficient in the 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).
Key Interfaces
- Lead Product Data Scientist.
- Other Data Scientists.
- Business stakeholders and users.
- Software engineers (front-end, back-end, DevOps, data engineers).
- Product & change managers.
- BA Digital teams (e.g., architects, application support managers).
- External partners and third parties, as required.
- ODS Leadership (Head of Data & Analytics, Head of iOps & Optimisation, Director of ODS).
Key Performance Indicators
- Model accuracy, performance, and runtime (precision, recall, accuracy).
- Time to develop and deploy features and models.
- Data ingestion & processing efficiency and robustness.
- Code quality and robustness (e.g., unit test coverage).
- Collaboration and cross-functional teamwork.
Data Scientist employer: Qualient Technology Solutions UK Limited
Contact Detail:
Qualient Technology Solutions UK Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python, machine learning, and data visualisation. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both technical and non-technical audiences.
✨Tip Number 4
Don't forget to apply through our website! We love seeing applications directly from candidates who are excited about joining our team. Plus, it shows you're genuinely interested in what we do.
We think you need these skills to ace Data Scientist
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’ve used Python and other programming languages to tackle real-life problems, so don’t hold back!
Be Specific About Your Experience: When detailing your experience, focus on the tools and platforms you've worked with, like AWS, Git, or Docker. We love specifics, so mention any projects where you’ve applied data science or machine learning techniques to achieve results.
Communicate Clearly: Remember, we value clear communication! Make sure your application conveys complex technical concepts in a way that’s easy to understand. This will show us you can bridge the gap between tech and business effectively.
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It’s the best way for us to receive your application and get to know you better. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Qualient Technology Solutions UK Limited
✨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 your Python skills and be ready to discuss how you've used libraries like scikit-learn, pandas, and numpy in real-life projects.
✨Showcase Your Problem-Solving Skills
Prepare to talk about specific examples where you've applied data science techniques to solve business problems. Be ready to explain your thought process, the trade-offs you considered, and how you arrived at your solutions.
✨Familiarise Yourself with Cloud Tools
Since experience with cloud platforms like AWS is preferred, make sure you understand the basics of tools like SageMaker and MLflow. If you’ve worked with CI/CD tools or containerisation, have some examples ready to share.
✨Communicate Clearly and Collaboratively
Practice explaining complex technical concepts in simple terms, as you’ll need to communicate with both technical and non-technical stakeholders. Highlight your teamwork experiences and how you’ve given and received feedback in past roles.
