At a Glance
- Tasks: Innovate ML algorithms to boost advertising performance and collaborate on exciting projects.
- Company: Join StackAdapt, a leading tech company transforming marketing with AI.
- Benefits: Competitive salary, commission structure, generous vacation, and retirement matching.
- Other info: Friendly, collaborative environment with opportunities for growth and innovation.
- Why this job: Make a real impact in the AI space while working remotely from anywhere in the UK.
- Qualifications: Masters or PhD in relevant fields and strong coding skills required.
The predicted salary is between 36000 - 60000 £ per year.
StackAdapt is the leading technology company that empowers marketers to reach, engage, and convert audiences with precision. Our marketing platform connects brand and performance marketing to drive measurable results across the customer journey. We are expanding our data science efforts and are seeking a talented Data Scientist to join our engineering team. Our platform connects to thousands of publishers and advertisers worldwide, handling millions of requests per second and making billions of decisions. We use the latest technologies to solve challenges in traffic, data storage, machine learning, and scalability.
StackAdapt is a remote-friendly company and we are open to candidates located anywhere in the United Kingdom for this position.
What You’ll Be Doing
- Innovate ML algorithms to maximize ROI and advertising performance, ranging from creating new algorithms to improving state-of-the-art methods and applying deep knowledge of classic methods.
- Write production code, collaborating with Data Engineers, to implement novel ML algorithms.
- Prototype potential algorithms and pipelines, test them on historical data, and iterate based on insights.
What You’ll Bring To The Table
- Masters degree or PhD in Computer Science, Statistics, Operations Research, or a related field; dual degrees are a plus.
- Ability to take ambiguously defined tasks and break them down into actionable steps.
- Comprehensive understanding of statistics, optimization, and machine learning.
- Proficiency in coding, data structures, and algorithms.
- Enjoy working in a friendly, collaborative environment with others.
StackAdapt’s Benefits
- Highly competitive salary + commission structure
- RRSP/401K matching
- 3 weeks vacation + 3 personal care days + 1 Culture
Applied Machine Learning Scientist employer: StackAdapt
StackAdapt is an exceptional employer that champions a remote-first culture, offering a collaborative and inclusive environment for its employees. With competitive salaries, comprehensive health benefits from day one, and robust support for personal development, including access to training and certifications, StackAdapt empowers its team members to grow and thrive. The company's commitment to diversity and employee well-being, alongside exciting social events, makes it a fantastic place for Machine Learning Engineers looking to make a meaningful impact in the tech industry.
StudySmarter Expert Advice🤫
We think this is how you could land Applied Machine Learning Scientist
✨Tip Number 1
Network like a pro! Reach out to current or former employees at StackAdapt on LinkedIn. A friendly chat can give us insider info about the company culture and maybe even a referral!
✨Tip Number 2
Show off your skills! Prepare a portfolio of your machine learning projects. Whether it's a GitHub repo or a personal website, let us see what you've done and how you think!
✨Tip Number 3
Practice makes perfect! Get ready for technical interviews by solving coding challenges and discussing ML concepts. We recommend platforms like LeetCode or HackerRank to sharpen those skills.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to connect directly with us!
We think you need these skills to ace Applied Machine Learning Scientist
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Applied Machine Learning Scientist role. Highlight your relevant experience with ML algorithms and coding skills, and don’t forget to mention any projects that showcase your ability to innovate and collaborate.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about data science and how your background aligns with StackAdapt’s mission. Be sure to mention specific technologies or methods you’ve worked with that relate to the job.
Showcase Your Problem-Solving Skills:In your application, give examples of how you've tackled ambiguous tasks in the past. We love candidates who can break down complex problems into actionable steps, so share your thought process and the outcomes of your efforts.
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to attach all your documents in one go. Plus, we love seeing applications come through our own platform!
How to prepare for a job interview at StackAdapt
✨Know Your Algorithms
Brush up on your machine learning algorithms and be ready to discuss how you've applied them in past projects. StackAdapt values innovation, so think of examples where you’ve created or improved algorithms to drive performance.
✨Showcase Your Coding Skills
Prepare to demonstrate your coding abilities during the interview. You might be asked to solve a problem on the spot, so practice coding challenges that involve data structures and algorithms relevant to machine learning.
✨Understand the Company Culture
Familiarise yourself with StackAdapt's team culture by checking out their resources. Be ready to discuss how you can contribute to a friendly, collaborative environment and why you’re excited about working remotely with their team.
✨Prepare Questions
Have insightful questions ready for your interviewers. Ask about their current data science projects or how they measure the success of their ML algorithms. This shows your genuine interest in the role and the company.
