Data Science Engineer - 12 month FTC

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Job description

Who we're looking for

Data Science Engineer, key responsibilities (platform dev, product dev), convey seniority roles (stakeholder, analysis leading delivery, balancing needs of user). Attention, team player, technical knowledge and expertise.

About Schroders

We're a global investment manager. We help institutions, intermediaries and individuals around the world invest money to meet their goals, fulfil their ambitions, and prepare for the future.
We have around 6,000 people on six continents. And we've been around for over 200 years, but keep adapting as society and technology changes. What doesn't change is our commitment to helping our clients, and society, prosper.

The base

We moved into our new HQ in the City of London in 2018. We're close to our clients, in the heart of the UK's financial centre. And we have everything we need to work flexibly.

The team

The Data Insights Unit (DIU)'s mission is to bring scientific rigour to all business decisions in Schroders. In essence we do this by:
1. making available new data sources,
2. unlocking the value in data by providing a research service, answering business questions by analysing these datasets,
3. scaling the value in data by building Insight Products: generalising those analyses or anticipating those questions by alerting people to relevant changes before they know to ask.
Through all these we use specialist Data Science tools and techniques: cloud technologies, machine learning, statistical techniques, and insights from the world of behavioural science.
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 rare insights. The DIU aims to find these new and potentially unorthodox datasets, extract the rich, hidden information they contain and use their expertise to improve traditional fundamental research.
Data Science Engineering team supports those goals by building and providing Data Science Platform and capabilities to support full life-cycle of an Insight Product - this includes insight discovery and development, its management as well as consumption.
We also contribute directly to product development by embedding withing cross functional product teams.

What you'll do

• Designing, developing and delivering Data Science Platform and associated capabilities
• Understanding and synthesising Data Science Platform users' needs and requirements and turning those into agile delivery items
• Actively promoting and implementing proper platform and data science engineering practices, approaches and technologies amongst internal platform team and product teams
• Collaborating with other delivery stakeholders (cloud infrastructure, data engineering, enterprise data) to identify and integrate shared components and capabilities (data access, data cataloguing, lineage tracking)
• Contributing to peer code, design reviews, delivery planning and preparation of releases for the platform and insight products

The knowledge, experience and qualifications you need

• Experience designing, developing and delivering software products on cloud platforms (AWS preferably) for data science and machine learning workflows (eg: python, git, unit testing, ci/cd, sagemaker, glue, metaflow, kubeflow, jupyter)
• Development of infrastructural libraries and frameworks to support data discovery, transformation and rigorous statistical and machine learning model development and serving (eg: mlflow, great expectations, model tuning and monitoring )
• Developing data transformation workflows with best practices for data versioning, cataloguing, lineage tracking (eg: spark, pandas, dbt, airflow, dagster)
• Familiarity with agile practices and experience product-centered development

The knowledge, experience and qualifications that will help

• Understanding and experience with development lifecycle of machine learning models - training, evaluation, validation and hosting
• Understanding of traditional/statistical data science or bioinformatics workflows and techniques (eg: snakemake, scikit learn pipelines, tidyverse)

What you'll be like

• Ability to work on own initiative, managing deadlines and prioritising.
• Pragmatic and proactive, willing to take localized action yet understanding bigger picture.
• Comfortable with ambiguity but taking actions towards reducing it.
• Comfortable with listening to and understanding different needs of stakeholders and users (data scientists, analysts and engineers) of the platform, yet being able to balance and communicate shared needs.

We're looking for the best, whoever they are

Schroders is an equal opportunities employer. You're welcome here whatever your socio-economic background, race, sex, gender identity, sexual orientation, religious belief, age or disability.