Data Consultant

The Data Insights Unit (DIU) are focused on applying data science skills and technologies to investment research, enhancing and complement the traditional ‘investor’ skills of our fund managers and analysts. A core part of this is the introduction of ‘alternative data’ into investment research. These are big, unstructured or novel data sets that are not already provided through traditional channels such as research from investment banks or through the Bloomberg terminals that every investment analyst or fund manager already has on their desktop.
The quantity of information available for investment research purposes is increasing at such a rate that traditional industry practices and skillsets are unable to absorb and process it. Global trends in digitalisation, social media, open data and technology are all creating vast streams of alternative data that are often highly unstructured and obscure. However, they contain valuable and often unique insights. The Data Insights team aims to find these new and potentially unorthodox datasets, extract the rich, hidden information they contain and use their expertise to enhance traditional fundamental research. As such it is providing Business Insights about the companies that we invest in.

The DIU has recently been asked to extend its remit to also provide advanced analytics expertise in other parts of Schroders, such as Sales, Product, HR, Operations and Digital. This is a natural extension of this Business Insights work.

Overview of role

The Data Consultant will be responsible for delivering insights to our internal customers, either investment professionals or other Schroders business divisions. These will take the form of analysis reports and presentations, the creation of prototype tools and dashboards using tools like Tableau, and involvement in the creation of other enabling analyses and tool sets. Naturally, this requires a blend of skills including data analysis and statistics as well as communication and business experience – the role is essentially acting as the bridge between the business and the data.

Responsibilities

1. Delivery of ad-hoc analysis requests delivered in tools such as Tableau, D3, Excel, R or Python, and making use of our data warehouse using SQL.
2. Creation of prototype self-service analytical tools and dashboards. This would be in cases where a question or request is likely to be repeated by other parts of the business or at other times.
3. Contributing towards the creation of a set of tools, techniques and practices that maximise the impact and efficiency of the team in…
a. turning raw data into useful and analysable derived data sets, and
b. developing re-usable analytical and visualisation methods
4. Work with team members and IT to develop and enhance the technical tools used as part of our work including the data warehouse and interconnections with various internal data systems.

Ideal Profile

Has a natural flair for insightful analysis. An interest in investment management or finance, would be passionate to provide insight and clear information to others so they can make better investments. Driven by a desire to make a difference by helping others, not least by understanding their needs and interests. Fascinated by the problem of how to make analysis useful and actionable.
A strong statistician, who can technically adapt to a number of software tools, be able to perform and discuss a number of statistical techniques that can assist different projects and company requirements.
Experience in data visualisation, exposure to tools such as Tableau or D3. Ability to communicate technical information to a wide variety of departments across the business to provide actionable impact.

Skills / Experience

Essential

• Strength in Maths, Statistics
• Influencing senior business stakeholders
• Creating clear and effective visual displays of data
• Creative thinking
• Commercial focus
• Pragmatic, action-oriented

Useful

• Relevant degree subject (e.g. Statistics, OR, Data Science, any Science)
• Meaningful experience with using SQL-based Data Warehouses
• R / Python
• Tableau
• Predictive analytics / machine learning
• Asset Management / Investment practices