Welcome To Data Science @ HL

Data science is about building intelligence and Hargreaves Lansdown uses this to gain new insights every second. The data science team started small, one person in fact (more on his journey in another post later) and are now an established team of four that tackle a wide range of business problems.

The Role

The title of “data scientist” is a relatively recent invention although the problems data scientists have been tasked with solving have always been around.

As we see it there are two fundamental problems that data science attempts to solve:

1. How can we make intelligent decisions for the future in an uncertain world?
2. Data is noisy and chaotic; how do we get any knowledge from it?

These are expressed in the 3 (or however many there are now) Vs of Big Data (volume, variety and velocity) and are addressed by all of the data consultancy buzzwords we know and love: AI, machine learning, predictive analytics, and so on. However, beneath all of this jargon we find that data science is focused around making intelligent decisions or gaining knowledge when everything is noisy and uncertain.

HL’s Feature Library

To keep things simple we see data science at HL as the creation and maintenance of a library of features.

Features provide us with valuable information about our clients and the business. Similar to an actual library we group our features into the following genres:

1. Intelligent features that predict a specific outcome (e.g. how many calls are we going to receive tomorrow at 9am).
2. Knowledgeable features that have been derived from the data (e.g. when do we receive the most inbound phone calls)

These features enable us to answer actual business problems such as:

• How many new colleagues do we need to recruit for the helpdesk to cope with the growth of the business?
• How do we make sure our investment in marketing is used most effectively?
• Are there patterns in how our roughly 1 million clients behave that could help us make things easier or provide a more personal experience?

These problems, big and small, can be addressed by expanding this feature library and over the next series of posts we will discuss our armory, how we prototype, how the team got into data science, and how we tackle specific problems

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