Data Scientist

Who we are:

Nutmeg is Europe’s leading Digital Wealth Manager, but we don’t want to stop there. We’re continuing to build our platform to help us achieve our mission of being the most trusted Digital Wealth Manager in the world. In 2021 Nutmeg became a J.P.Morgan company offering investments and digital wealth management services to consumers, complementing Chase’s digital bank in the UK.

Since being founded we've:

• Grown to 180+ employees
• Launched 4 amazing products including JISA and Lifetime ISA
• Won multiple awards including Best Online Stocks & Shares ISA Provider for the fifth year in a row!

We hit the 170,000 investor milestone in 2021 and now manage over £4 billion AUM.

*We offer flexible working*

Job in a nutshell:

This is a fantastic opportunity for an experienced data scientist with an analytical background to join and develop their career in a market leading FinTech company.

In this role you will be scoping, prioritizing, and delivering data products that require extensive knowledge in supervised learning, reinforcement learning and experimentation. You will also write production-ready Python code and possess a high sense of commercial awareness to ensure your deliverables have an impact to Nutmeg’s core KPIs.

You will be part of multiple domains and work closely with domain experts embedded in the product team while keeping aligned with the product’s vision and strategy, with a particular focus on personalisation and optimisation of the customer experience.

At Nutmeg, you will find a fast-paced and transparent environment where data products are delivered at short iterations, where results are measurable, and credit is given whenever due.

Requirements

Your skills:

• You have extensive experience in solving and productionizing supervised machine learning solutions.
• You have a good understanding of reinforcement learning methodologies, including bandit algorithms.
• Highly proficient in SQL and Python with plenty of commercial experience in scientific computing and machine learning libraries (NumPy, SciPy, Pandas, Matplotlib and Scikit-Learn).
• You don’t just apply out-of-the-box models, you know how they work, when they work and why they work. You are a critical thinker.
• You’ll be the kind of person is comfortable working alone and as part of closely collaborative teams. You enjoy sharing your work and how you got there.
• Extensive working experience with tools and frameworks such as Airflow, Docker, Rest APIs (Flask, Fast API), Github, Jenkins, ArgoCD, and familiarity working in a CI/CD environment.
• You are truly scientific: excellent understanding of frequentist and Bayesian A/B testing. Commercial experience in designing complex experiments.
• An entrepreneurial mind-set. Figuring out how to improve business processes and finding opportunities to apply data science techniques to real problems is as exciting to you as solving them.
• You’ll be comfortable taking complex ideas and communicating them to different audiences.
• MSc or PhD in a quantitative field such as Maths, Statistics, Operational Research, Computer Science or Behavioural Science.

You might also have:

• Working experience with common MLOps tools such as MLFlow and DCV and managed services such as AWS.
• Deep understanding of advanced statistical models such as Generalised Linear Models, Linear Mixed Effect Models, and Survival Analysis.
• Experience with advanced experimental design, including sequential experimentation and multiple testing.
• Understanding of time-series forecasting methods such as Bayesian Structural Time Series models and ARIMA, and familiarity with packages such as Causal Impact or Prophet.
• Comprehensive understanding of, and expertise applying, object-oriented programming concepts.