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- Tasks: Innovate and implement cutting-edge ML algorithms to enhance advertising performance.
- Company: Join StackAdapt, a leading tech company transforming marketing with AI.
- Benefits: Enjoy competitive salary, generous vacation, health benefits, and remote work options.
- Why this job: Make a real impact in the fast-paced world of AI and marketing.
- Qualifications: Masters or PhD in relevant fields; strong coding and machine learning skills required.
- Other info: Be part of a diverse, inclusive team with excellent growth opportunities.
The predicted salary is between 28800 - 48000 £ 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 & Belief day + birthdays off
- Access to a comprehensive mental health care platform
- Health benefits from day one of employment
- Work from home reimbursements
- Optional global WeWork membership for those who want a change from their home office
- Robust training and onboarding program
- Support for personal development initiatives (conferences, courses, etc)
- Access to StackAdapt programmatic courses and certifications to support continuous learning
- Parental leave, a friendly and welcoming culture
- Social and team events
StackAdapt is a diverse and inclusive team of collaborative, hardworking individuals. We acknowledge and welcome people from all backgrounds and identities. If you have any requests or requirements to support you throughout any part of the interview process, please let our Talent team know.
Seniority level: Entry level
Employment type: Full-time
Job function: Engineering and Information Technology
Industries: Technology, Information and Internet
Applied Machine Learning Scientist employer: StackAdapt
Contact Detail:
StackAdapt Recruiting Team
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. When you get that interview, we want to see how you’ve tackled real-world problems and what innovative solutions you've come up with.
✨Tip Number 3
Practice makes perfect! Get comfortable with common technical interview questions related to machine learning and coding. We suggest doing mock interviews with friends or using online platforms to sharpen your skills.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows us you’re genuinely interested in joining the StackAdapt team!
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 relevant experience, especially in ML algorithms and coding. We want to see how your skills align with what we do at StackAdapt!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how you can contribute to our team. Keep it friendly and professional, just like us at StackAdapt.
Showcase Your Projects: If you've worked on any cool projects or prototypes, make sure to mention them! We love seeing practical applications of your skills. Include links to your GitHub or any relevant portfolios if you have them.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
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 about how you can improve existing methods or propose new ones.
✨Showcase Your Coding Skills
Prepare to demonstrate your coding abilities during the interview. You might be asked to solve problems on the spot, so practice writing clean, efficient code that reflects your understanding of data structures and algorithms.
✨Understand the Business Impact
Be ready to explain how your work as a Data Scientist can drive ROI and enhance advertising performance. StackAdapt is all about measurable results, so think about how your algorithms can make a difference in real-world scenarios.
✨Emphasise Collaboration
Since you'll be working closely with Data Engineers, highlight your experience in collaborative environments. Share examples of how you've successfully worked in teams to tackle complex problems and iterate on solutions.