Data Scientist (Machine Learning Engineer)
The City, City (EC4)
£70,000 - £80,000 per annum
Data Scientist, Data Science Engineer, Machine Learning Engineer
Location: London Bridge
Salary: £70,000 - £80,000 + Equity + benefits.
They are a very friendly Tech Product Company, 5 minutes walk from London Bridge station and have created a collaborative and supportive environment who hold Friday TED talks (and supply food!), have a nice pub on their doorstep and offer flexible/remote working as well as an employee share scheme, regular pay reviews, Pension scheme, quarterly dinners, birthday cakes and social events plus they're always on the lookout for creative ways to look after you and will encourage you to go to them with new idea or needs.
They're looking for a Data Scientist / Machine Learning Engineer to lead their Data strategy and team. As the Data Scientist you'll need to have strong Machine Learning experience, ideally along with Deep Learning, in order to mentor and train the junior-mid level Data Scientists within the current team.
About the Data Scientist role...
Data Scientist key attributes:
- As the Data Scientist you'll report to the CTO, create deep actionable insights, visualise data, and build models for all aspects of their products. Utilising diverse data from commercial insurance, technical pricing and operational policy performance by engagement with customers and internal teams as well as lead and grow their data science expertise.
- This is an exciting and rewarding role requiring a smart, disciplined and experienced Lead Data Scientist who is statistically savvy with a very strong technical background in applying Machine Learning and data analytics techniques.
- The Lead Data Scientist role will have the potential to grow into Director of Data Science role over time.
Data Scientist tools:
- Machine Learning (& Deep Learning)
- Data Science/ML model presentation experience
- Knowledge in: Risk Statistical Modelling, Machine Learning and Predictive Modelling, and Data mining
- Creative approach to building test hypothesis and statistical methodology to design experiments and interpret the outcomes
- Apache Spark (preferably with pySpark) and R (packages: dplyr, tidyR, Caret, H2O), and Python Pandas, Numpy, SciKit, programming languages (e.g. Java, Python, Ruby)
- Supervised Learning (Regression, Classification), Unsupervised (Clustering), Dimensionality Reduction, Model selection and optimisation, Feature selection, Metric selection, bootstrapping, Ensembling & Stacking methods.
If you're a Data Scientist who's keen to find out more, please apply now!