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- Tasks: Lead the development of algorithms for a high-scale advertising platform.
- Company: Dynamic UK eCommerce business focused on innovation.
- Benefits: Competitive salary, great perks, and opportunities for growth.
- Why this job: Shape the future of advertising with cutting-edge machine learning technology.
- Qualifications: Strong background in data science and experience with production ML systems.
- Other info: Join a collaborative team and make a real impact in the industry.
The predicted salary is between 48000 - 72000 £ per year.
A UK consumer eCommerce business is seeking a Lead Data Scientist to take technical ownership of their internal advertising and monetisation platform's algorithms. This strategic role entails leading the algorithm roadmap, making architectural decisions, and deploying machine learning models in a high-scale environment.
The ideal candidate will possess a strong data science background with proven experience in production ML systems, advanced skills in Python and SQL, and familiarity with tools like PyTorch or TensorFlow.
Competitive salary and benefits await.
Lead Data Scientist: Production ML for Scalable Ads employer: iO Associates
Contact Detail:
iO Associates Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Scientist: Production ML for Scalable Ads
✨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 best projects, especially those involving production ML systems. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your experience with Python, SQL, and tools like PyTorch or TensorFlow. Practice common data science interview questions to boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Lead Data Scientist: Production ML for Scalable Ads
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with production ML systems and your proficiency in Python and SQL. We want to see how your skills align with the role, so don’t hold back!
Tailor Your Application: Customise your CV and cover letter to reflect the specific requirements of the Lead Data Scientist position. Mention any relevant projects or experiences that demonstrate your ability to lead algorithm roadmaps and make architectural decisions.
Be Clear and Concise: When writing your application, keep it straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s necessary to showcase your expertise in tools like PyTorch or TensorFlow.
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for this exciting opportunity!
How to prepare for a job interview at iO Associates
✨Know Your Algorithms
Make sure you brush up on the algorithms relevant to advertising and monetisation. Be ready to discuss your experience with deploying machine learning models in production, as well as any architectural decisions you've made in past projects.
✨Showcase Your Technical Skills
Prepare to demonstrate your advanced skills in Python and SQL. You might be asked to solve a problem on the spot, so practice coding challenges that involve data manipulation and analysis using these languages.
✨Familiarity with Tools is Key
Since familiarity with tools like PyTorch or TensorFlow is essential, make sure you can talk about specific projects where you've used these frameworks. Highlight any challenges you faced and how you overcame them.
✨Strategic Thinking
This role involves leading the algorithm roadmap, so be prepared to discuss your strategic approach to data science. Think about how you would prioritise projects and make decisions that align with business goals, and be ready to share examples from your past experiences.
