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- Tasks: Develop machine learning models and optimise operations for decision-support software.
- Company: Join a leading tech firm at Heathrow with a focus on innovation.
- Benefits: Competitive daily rate, flexible working, and opportunities for professional growth.
- Why this job: Make a real impact in the tech industry while honing your data science skills.
- 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 and feedback.
The predicted salary is between 54000 - 90000 £ 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).
Marketing Data Scientist in City of London employer: Avensys Consulting UK
Contact Detail:
Avensys Consulting UK Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Marketing Data Scientist in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at local meetups. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects and optimisation models. This is your chance to demonstrate your expertise in Python and data science techniques.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss how you've applied data science in real-life scenarios, especially in transportation or operations.
✨Tip Number 4
Don’t forget to apply through our website! We’re always looking for talented individuals like you, and applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Marketing Data Scientist in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Marketing Data Scientist. Highlight your experience with machine learning, optimisation techniques, and any relevant programming skills. We want to see how your background aligns with what we're looking for!
Showcase Your Projects: Include specific projects where you've applied data science or machine learning techniques. Whether it's a personal project or something from your previous job, we love seeing real-life applications of your skills. Don't forget to mention the tools you used!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about this role and how your skills can contribute to our team. Keep it concise but impactful – we want to feel your enthusiasm!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It helps us keep everything organised and ensures your application gets the attention it deserves. Plus, it’s super easy!
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 your Python skills and be ready to discuss how you've applied libraries like scikit-learn and pandas in real-life scenarios.
✨Showcase Your Problem-Solving Skills
Prepare to talk about specific business problems you've tackled using data science. Think of examples where you had to choose between different approaches and explain why you made those choices. This will demonstrate your analytical skills and understanding of trade-offs.
✨Familiarise Yourself with Cloud Tools
Since experience with cloud platforms like AWS is preferred, make sure you can discuss any relevant projects you've worked on. If you’ve used tools like SageMaker or Docker, be ready to share your experiences and how they contributed to your projects.
✨Communicate Clearly and Collaboratively
Practice explaining complex technical concepts in simple terms. You might be asked to present your ideas to a non-technical audience, so being able to communicate effectively is key. Also, highlight your teamwork experiences and how you handle feedback.
