At a Glance
- Tasks: Design and implement machine learning solutions that drive real business impact.
- Company: Join a forward-thinking tech company focused on innovation and collaboration.
- Benefits: Flexible time off, health insurance, remote work support, and generous allowances.
- Other info: Dynamic team culture with opportunities for growth and professional development.
- Why this job: Be a key player in shaping the future of machine learning applications.
- Qualifications: 5+ years experience in machine learning, data science, and strong Python skills.
The predicted salary is between 60000 - 80000 £ per year.
We are looking for intellectually curious, highly motivated individuals to be foundational members of our Machine Learning and Data Platform team. You will partner across the company and use data to design scalable solutions based on a deep understanding of critical business goals. The ideal candidate will leverage data analysis, statistics and machine learning to lead initiatives end to end, including data & machine learning engineering.
What you'll do:
- Build and help set direction across the entire machine learning development process to implement machine learning algorithms in production, including exploratory data analysis, data modeling, feature engineering, model training and tuning, testing, deployment, and monitoring.
- Partner closely across the business to identify improvements and influence decisions using data science methodologies and tools.
- Develop new production machine learning algorithms and systems that enrich the app experience with machine learning-powered experiences.
- Contribute across the data science and machine learning development stack: idea development, opportunity sizing, prototyping, testing, and deployment.
- Design and implement end-to-end data pipelines and data systems that support MLOps and business processes.
- Build high quality communication devices such as dashboards, notebooks, documents, presentations to convey insights across a broad audience.
- Define and advance standard methodologies within an experiment-driven culture.
Bachelor's degree in Computer Science, a related field, or equivalent work experience.
Curious about who thrives at Whatnot? We’ve found that low ego, a growth mindset, and leaning into action and high impact goes a long way here.
As our next Machine Learning Scientist you should have 5+ years of experience, plus:
- Bachelor's degree in Computer Science, Statistics, Mathematics, Software Engineering or related technical field, or equivalent work experience.
- Industry experience with a track record of applying scientific methods to solve real-world problems on consumer scale data.
- Experience leading work to develop and deploy machine learning- and data-based solutions in production.
- Extensive experience with Python and SQL for data science, machine learning, and software development e.g. numpy, scipy, pandas, scikit-learn, PyTorch, LightGBM, Flask, FastAPI, Docker, Jupyter.
- Ability to work autonomously and lead initiatives across multiple product areas and communicate findings with leadership and product teams.
- Comfortability with data warehouses and transformation tools such as Snowflake, dbt, Dagster.
- Proficiency and experience in applied statistics and machine learning fields e.g. Experimentation and Causal Analysis, Recommendations, Fraud & Anomaly Detection, Natural Language, Computer Vision.
- Firm grasp of visualization tools, interactive and self-serving, such as dashboards and notebooks.
- Professionalism around collaborating in a remote working environment and well tested reproducible work.
- Above average documentation and communication skills.
The salary range may be inclusive of several levels that would be applicable to the position. Final salary will be based on a number of factors including, level, relevant prior experience, skills and expertise. This range is only inclusive of base salary, not benefits or equity in the form of stock options.
Benefits:
- Flexible Time off Policy and Company-wide Holidays (including a spring and winter break)
- Health Insurance options including Medical, Dental, Vision
- Work From Home Support
- $1,000 home office setup allowance
- $150 monthly allowance for cell phone and internet
- Care benefits
- $450 monthly allowance on food
- $500 monthly allowance for wellness
- $5,000 annual allowance towards Childcare
- $20,000 lifetime benefit for family planning, such as adoption or fertility expenses
- Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally
- Parental Leave: 16 weeks of paid parental leave + one month gradual return to work *company leave allowances run concurrently with country leave requirements which take precedence.
Machine Learning Scientist in London employer: Whatnot
Whatnot is an exceptional employer for Machine Learning Scientists, offering a dynamic work culture that fosters intellectual curiosity and collaboration across teams. With generous benefits such as flexible time off, comprehensive health insurance, and substantial allowances for home office setup and childcare, employees are supported in both their professional and personal lives. The company prioritises employee growth through a focus on experimentation and innovation, making it an ideal place for those looking to make a meaningful impact in the field of machine learning.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Scientist in London
✨Network Like a Pro
Get out there and connect with folks in the industry! Attend meetups, webinars, or even online forums. The more people you know, the better your chances of landing that Machine Learning Scientist role.
✨Show Off Your Skills
Create a portfolio showcasing your projects and achievements in machine learning. Use GitHub to share your code and document your processes. This will give potential employers a taste of what you can do!
✨Ace the Interview
Prepare for technical interviews by practising common machine learning problems and algorithms. Don’t forget to brush up on your Python and SQL skills, as they’ll likely come up during the interview process.
✨Apply Through Us!
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you to join our team. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Machine Learning Scientist in London
Some tips for your application 🫡
Show Your Curiosity:We want to see your intellectual curiosity shine through in your application. Share examples of how you've tackled complex problems or explored new technologies in your previous roles. This will help us understand your passion for machine learning and data science!
Tailor Your Experience:Make sure to highlight your relevant experience with machine learning and data analysis. Use specific examples that align with the job description, like projects where you implemented algorithms or built data pipelines. This helps us see how you can contribute to our team.
Communicate Clearly:Your written application is a chance to showcase your communication skills. Be clear and concise, and make sure to explain your thought process when discussing your projects. We value strong documentation and communication, so let that reflect in your application!
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 gives you a chance to explore more about our company culture and values!
How to prepare for a job interview at Whatnot
✨Know Your Algorithms
Brush up on your machine learning algorithms and be ready to discuss how you've implemented them in past projects. Be prepared to explain the reasoning behind your choices and how they align with business goals.
✨Showcase Your Data Skills
Prepare examples of your experience with data analysis, feature engineering, and model tuning. Use specific metrics or outcomes to demonstrate how your work has positively impacted previous projects.
✨Communicate Clearly
Practice explaining complex concepts in simple terms. You’ll need to convey insights to a broad audience, so focus on clarity and structure in your communication, whether it’s through dashboards or presentations.
✨Emphasise Collaboration
Highlight your experience working cross-functionally. Share examples of how you’ve partnered with different teams to drive initiatives and influence decisions using data science methodologies.
