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For Companies At a Glance
- Tasks: Design and deploy machine learning models to solve complex business problems.
- Company: Caribou is revolutionising international tax for global businesses, making it accessible and affordable.
- Benefits: Enjoy competitive salary, generous EMI options, health insurance, and custom WFH equipment setup.
- Why this job: Join a dynamic team tackling real-world challenges with innovative data-driven solutions.
- Qualifications: Master’s or Ph.D. in relevant fields; 3+ years in data science or machine learning required.
- Other info: Experience in early-stage startups is a plus!
The predicted salary is between 48000 - 84000 £ per year.
About Caribou
International tax is a rigged system. Tax rules get ever-more complicated, compliance gets more expensive, and the Big Four controls the expertise. They charge fees that only giant companies can afford, while smaller businesses are left flying blind. Caribou is a tax platform designed to make international tax accessible to every global business. We’re fixing Transfer Pricing first, where one million businesses are in need, but only ten thousand experts exist. Our backers include Y Combinator, Accel, Lakestar and angel investors who were founders or executives of leading companies in London and San Francisco.
About the Role
We’re looking for a Machine Learning Data Scientist to join our team. In this role, you will design, develop, and deploy machine learning models to solve complex business problems, leveraging large datasets from customer books and accounts to generate actionable insights for our transfer pricing team. You will collaborate with tax and accounting experts within our team to identify opportunities for data-driven innovation and ensure models are scalable, accurate, and impactful.
In this role, you will:
- Work closely with our tax/accounting team to understand financial processes with our customers
- Design and implement machine learning algorithms to assist us with these processes
- Evaluate model performance using appropriate metrics and optimize for accuracy, efficiency, and scalability
- Collaborate with engineers to integrate ML models into production systems
- Conduct exploratory data analysis to uncover trends, patterns, and opportunities for improvement
- Stay current with advancements in machine learning, AI, and data science methodologies to continuously improve processes and solutions
Requirements for the role:
- Master’s or Ph.D. in Computer Science, Statistics, Mathematics, Engineering, or a related field (Bachelor’s with significant experience also considered)
- 3+ years of experience in data science, machine learning, or a related role
- Proficiency in programming languages such as Python or R, and libraries like TensorFlow, PyTorch, Scikit-learn, or Pandas
- Strong understanding of statistical modeling, probability, and optimization techniques
- Ability to work with messy, real-world datasets and perform feature engineering
- Excellent problem-solving skills and attention to detail
- Strong communication skills to convey complex concepts to diverse audiences
Bonus points:
- You’ve worked in an early-stage startup before and understand the challenges
Perks & Benefits (for UK-based full-time employees):
- Competitive salary
- Generous EMI options
- 100% book subsidy
- Pension
- Health Insurance
- Custom WFH equipment setup
Contact Detail:
Caribou Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Data Scientist London
✨Tip Number 1
Familiarise yourself with Caribou's mission and the specific challenges they aim to solve in international tax. Understanding their focus on transfer pricing will help you tailor your discussions during interviews, showcasing how your skills can directly contribute to their goals.
✨Tip Number 2
Brush up on your knowledge of machine learning algorithms and be prepared to discuss how you've applied them in real-world scenarios. Highlighting your experience with large datasets and your ability to derive actionable insights will make you stand out as a candidate.
✨Tip Number 3
Network with professionals in the data science and tax fields, especially those who have experience in startups. Engaging with these communities can provide valuable insights into the role and may even lead to referrals within Caribou.
✨Tip Number 4
Stay updated on the latest trends in machine learning and AI, particularly in relation to finance and accounting. Being able to discuss recent advancements or case studies during your interview will demonstrate your passion for the field and your commitment to continuous learning.
We think you need these skills to ace Machine Learning Data Scientist London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning and data science. Focus on projects where you've designed and deployed models, and include specific programming languages and tools you've used, such as Python, TensorFlow, or Scikit-learn.
Craft a Compelling Cover Letter: In your cover letter, express your passion for making international tax accessible and how your skills can contribute to Caribou's mission. Mention any experience you have with financial processes or working in startups, as this aligns well with the role.
Showcase Your Projects: If you have a portfolio of projects or GitHub repositories, include links in your application. Highlight any machine learning algorithms you've implemented and the impact they had on previous projects, especially those related to data analysis or financial insights.
Prepare for Technical Questions: Be ready to discuss your technical skills in detail during the interview process. Brush up on statistical modeling, optimization techniques, and your experience with real-world datasets. Prepare examples that demonstrate your problem-solving abilities and attention to detail.
How to prepare for a job interview at Caribou
✨Understand the Business Context
Before your interview, make sure you understand Caribou's mission and how machine learning can impact international tax. Familiarise yourself with transfer pricing and the challenges faced by smaller businesses in this area.
✨Showcase Your Technical Skills
Be prepared to discuss your experience with programming languages like Python or R, and highlight specific projects where you've used libraries such as TensorFlow or Scikit-learn. Bring examples of your work that demonstrate your proficiency in machine learning algorithms.
✨Prepare for Problem-Solving Questions
Expect to tackle real-world problems during the interview. Brush up on your statistical modelling and optimisation techniques, and be ready to explain your thought process when solving complex data challenges.
✨Communicate Clearly
Since you'll be collaborating with tax and accounting experts, practice explaining complex concepts in simple terms. Good communication skills are essential, so think about how you can convey your ideas effectively to a diverse audience.
