Data Science Steward

Location:Greater London
Job Type:Full Time

Data is the lifeblood of business decisions throughout Schroders. Whilst the DIU is aware of the importance of good data engineering, data governance, data management and data cataloguing in order to extract the most value from data with the least friction and risk, these are shared concerns rather than sitting with any one individual at present. You would be assisting in projects to actively improve the quality and integrity of our data and tooling as an investment in future capabilities, working alongside the data scientists and data engineers.

There are 3 main areas you will focus on: Data on-boarding and governance, data scouting and technology adoption.

The arrival of our new AWS research environment (Insights workshop) offers us a chance to document standardised ways of working up front to enable the telemetry of data assets across the data scientists. A governance oversight is required.

The imminent arrival of the DIU’s AWS Insights workshop requires an individual to drive adoption of the new tool stack and remain at the forefront of best practises in its usability. New tools such as the testing framework also require DIU ownership to drive into the data scientists’ ways of working.

The DIU will continue to scout for data. We use third parties to help here, some of whom offer workflow management tools, which will allow for a more devolved way of working relating to scouting activities. However a centralised ownership is still required for tracking and tool management which the data science steward will be responsible for.


You will report to Olivia Regan (Head of Data Enablement, Data Insights).


As part of the role you will be building relationships with other business teams and with our colleagues in the Technology function, ensuring that the various initiatives align and help each other.

Responsibilities


1. Reviewing and documenting DIU data flows for larger projects. This will help us identify commonly used tables and attributes and assist in the prioritisation to the Insights Data Engineering (IDE) team.
2. Documenting of standards in AWS and enforcing governance in the DIU. Working with internal teams to co-ordinating the refresh of internal data stores into the data lake.
3. Data pipelining of trial data and initial evaluation where feasible.
4. Owning the workflow tool for data feeds and keeping track of DIU data assets using knowledge management and collaboration tools.
5. Owning the testing framework for DIU – identifying use cases, prioritising new features
6. Assisting with data science refactoring / migration tasks


Team Culture


The Data Insights Unit is a friendly, dynamic and exciting team, and we welcome applications from a diverse range of individuals at all levels of hiring. Schroders has strong support for flexible working practices, including modern technology for remote working. As such we offer a friendly and welcoming home for diverse and neuro-diverse individuals with various home/family arrangements.

The DIU consciously adopts a strengths-based style of management and personal development. We strive to understand people’s motivations and strengths and orient their roles to play to these. This applies to their immediate role and as part of their ongoing career development into future roles.


Schroders offers exceptional benefits, including excellent pension and flexible benefits worth roughly 25% of base salary in total. In addition all team members benefit from Schroders’ bonus scheme. In this, bonus is shared out across the DIU team with far more weight on team performance than individual performance – we all succeed as a team, and so we share in the rewards as a team. Further details can be given on request.

Schroders is an equal opportunities employer and welcomes applications regardless of sex, marital status, ethnic origin, sexual orientation, religious belief or age.

Skills / Experience


Essential


  • Commercial focus
  • Pragmatic, willing to take action
  • Good communication and influencing skills
  • Great at creating structure and getting things organised
  • Meaningful skill/experience in…
  • SQL and relational databases
  • data modelling (preferably including data warehouse/OLAP modelling approaches)
  • at least one programming language (not including SQL or similar)
  • Relevant degree (e.g. computer science, information/library science, mathematics/OR/statistics or a solid quantitative discipline in science, business or social science)
  • Ability to work well as part of a team


Nice to have


  • Use of Tableau for data visualisation. Experience with PowerBI / Qlikview / Spotfire also useful.
  • Librarian Science or other curatorial practice.
  • Experience working closely with or supporting a data analytics/data science/BI/MI function
  • ETL / Data Pipelining / Data Engineering skills
  • Statistical analysis and interpretation
  • Programming/analysis scripting experience in R or Python.
  • Linux experience and shell scripting.
  • Big data experience such as Hadoop, Spark, Hive.
  • Asset Management / Investment practices
  • AWS knowledge