At a Glance
- Tasks: Lead the development of innovative ML features and mentor fellow engineers.
- Company: Join a passionate team at a cutting-edge tech company focused on machine learning.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic environment with a focus on collaboration and innovation.
- Why this job: Make a real impact by applying advanced ML technologies to solve customer problems.
- Qualifications: 5-8 years of applied ML experience and strong programming skills required.
The predicted salary is between 70000 - 90000 £ per year.
Fin's Machine Learning team is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers’ hands. We are an extremely product focussed team, working in partnership with Product and Design functions of teams we support. Our team's dedicated ML product engineers enable us to move to production fast, often shipping to beta in weeks after a successful offline test. We are very passionate about applying machine learning technology, having productized everything from classic supervised models to cutting-edge unsupervised clustering algorithms and novel applications of transformer neural networks. We test and measure the real customer impact of each model we deploy.
What will I be doing?
- Play an active role in hiring, mentoring and career development of other engineers
- Raise the bar for technical standards, performance, reliability, and operational excellence
- Identify areas where ML can create value for our customers
- Identify the right ML framing of product problems - Working with teammates and Product and Design stakeholders
- Conduct exploratory data analysis and research - Deeply understand the problem area
- Research and identify the right algorithms and tools - Being pragmatic, but innovating right to the cutting-edge when needed
- Perform offline evaluation to gather evidence an algorithm will work
- Work with engineers to bring prototypes to production
- Plan, measure & socialize learnings to inform iteration
- Partner deeply with the rest of the team, and others, to build excellent ML products
What skills might I need?
- 5-8 years applied ML experience
- Previous background in a senior/staff role (data science, software development or academic)
- Significant, demonstrated impact that your work has had on the product and/or the teams
- Strong programming skills
- Experience as the primary technical leader for a team
- Strong communication skills, both within engineering teams and across disciplines
- Comfort with ambiguity
- Typically have advanced education in ML or related field (e.g. MSc)
- Scientific thinking skills
Bonus skills & attributes
- Track record shipping ML products
- PhD or other experience in a research environment
- Deep experience in an applicable ML area - E.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering
- Strong stats or math background
- Visualization, data skills, SQL, matplotlib, etc.
Staff Machine Learning Scientist employer: Fin
Fin is an exceptional employer for a Staff Machine Learning Scientist, offering a dynamic and product-focused work environment where innovation thrives. With a strong emphasis on collaboration across teams, employees benefit from mentorship opportunities and the chance to lead impactful projects that directly enhance customer experiences. Located in a vibrant tech hub, Fin provides access to cutting-edge resources and a culture that values continuous learning and professional growth.
StudySmarter Expert Advice🤫
We think this is how you could land Staff Machine Learning Scientist
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, prototypes, or any relevant work. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts in simple terms, as communication is key when working with cross-functional teams.
✨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, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace Staff Machine Learning Scientist
Some tips for your application 🫡
Show Your Passion for ML:Let us see your enthusiasm for machine learning! In your application, highlight any projects or experiences that showcase your love for the field. We want to know how you've applied ML in real-world scenarios and the impact it had.
Tailor Your Application:Make sure to customise your CV and cover letter for this role. Use keywords from the job description and align your skills with what we’re looking for. This shows us you’ve done your homework and are genuinely interested in joining our team.
Highlight Team Collaboration:Since we work closely with Product and Design teams, emphasise your experience in collaborative environments. Share examples of how you’ve partnered with others to deliver successful ML products, as teamwork is key to our success!
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 don’t miss out on any important updates. Plus, it makes the process smoother for both of us!
How to prepare for a job interview at Fin
✨Know Your ML Stuff
Make sure you brush up on your machine learning knowledge, especially the algorithms and technologies mentioned in the job description. Be ready to discuss your past projects and how you've applied different ML techniques to solve real-world problems.
✨Showcase Your Team Spirit
Since this role involves working closely with Product and Design teams, highlight your experience in cross-functional collaboration. Share examples of how you've partnered with other disciplines to deliver successful ML products.
✨Prepare for Technical Questions
Expect some deep dives into your technical skills. Be prepared to explain your thought process when identifying the right ML framing for product problems and how you approach exploratory data analysis. Practise articulating your methods clearly.
✨Demonstrate Your Impact
Be ready to discuss specific instances where your work has made a significant impact on a product or team. Use metrics and outcomes to illustrate your contributions, as this will show your potential value to the company.
