At a Glance
- Tasks: Design and implement machine learning solutions that drive real business impact.
- Company: Join a forward-thinking tech company with a collaborative and innovative culture.
- Benefits: Enjoy flexible time off, health insurance, and generous allowances for home office and wellness.
- Other info: Remote-friendly environment with excellent career advancement opportunities.
- Why this job: Be at the forefront of machine learning and shape the future of technology.
- Qualifications: 5+ years in machine learning, strong Python and SQL skills, and a growth mindset.
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.
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
Machine Learning Scientist employer: Whatnot
At Whatnot, we pride ourselves on fostering a culture of innovation and collaboration, making us an exceptional employer for Machine Learning Scientists. Our flexible work environment, comprehensive benefits, and commitment to employee growth ensure that you can thrive both personally and professionally while contributing to impactful projects. With generous allowances for home office setup, wellness, and childcare, along with a supportive remote working culture, you'll find meaningful opportunities to advance your career in a dynamic and inclusive setting.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Scientist
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. 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 machine learning projects, data analyses, and any cool algorithms you've developed. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and practising common interview questions. Don’t forget to highlight your experience with Python, SQL, and any relevant tools you’ve used. Confidence is key!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team. Let’s get you that Machine Learning Scientist role!
We think you need these skills to ace Machine Learning Scientist
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 your work with Python, SQL, or any machine learning frameworks. This helps us see how you can contribute to our team right away!
Communicate Clearly:Since communication is key in our remote environment, ensure your application is well-structured and easy to read. Use clear language and avoid jargon where possible. We appreciate candidates who can convey complex ideas simply and effectively!
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
Make sure you can demonstrate your proficiency in Python and SQL. Bring examples of your work, like data pipelines or dashboards, to showcase your ability to turn data into actionable insights.
✨Communicate Clearly
Practice explaining complex concepts in simple terms. You’ll need to convey your findings to a broad audience, so being able to communicate effectively is key. Use visuals if possible to illustrate your points.
✨Emphasise Collaboration
Highlight your experience working across teams and how you’ve influenced decisions using data. Companies value candidates who can partner effectively, so share specific examples of successful collaborations.
