Senior ML Engineer
4 weeks ago
This job is brought to you by Jobs/Redefined, the UK's leading over-50s age inclusive jobs board.
Introduction
Job Title:
Senior ML Engineer
Business Unit:
Technology / Engineering
Location:
London
Direct Reports:
0
Reporting Manager Title:
Bogdan Vrusias
Date Prepared:
01 / 05 / 2024
Section 1 - Company Intro
Who we are
We are an organisation that exists to drive progress. That's the "red thread" that connects everyone at The Economist Group (TEG). Our businesses share a devotion to innovation, independence and rigour in their fields of expertise. We empower people to understand and tackle the critical challenges and changes facing the world. Our analytical rigour, global expertise and evidence-based insights enable individuals and organisations to make sense of these shifts and chart a course through them.
Our story began in 1843, when a Scottish hat manufacturer founded The Economist newspaper to further the cause of free trade. Since then, we have evolved into a staunchly independent global media and information-services company that provides intelligent thinking for an international audience. We deliver our analysis and insights in many formats to subscribers and clients in 170 countries with four businesses, each of which upholds our global reputation for excellence and integrity.
Section 2 - Business Unit Intro
Our team of 200 colleagues is at the heart of The Economist Group's digital-first agenda. Together, we are delivering user-friendly, high-quality digital products that bring data, intelligence and analysis to our growing global audiences across all our four businesses.
Our team develops cutting-edge products that provide valuable insights and analysis to business and government leaders worldwide. Whether it involves creating new products for the Economist Intelligence Unit (EIU), conceptualising mobile applications that deliver personalised content, or enhancing our flagship website, economist.com, the Technology team plays a crucial role in shaping how we attract, convert, engage, and retain our 1.2 million subscribers.
The Technology team collaborates closely with other teams to engineer innovative solutions that cater to the evolving needs of our subscribers. This includes leveraging advanced technologies and methodologies to enhance the acquisition of new subscribers, optimise conversion rates, deliver engaging user experiences, and ensure long-term subscriber loyalty.
Section 3 - Job Description
The role:
We are looking for a Senior Machine Learning (ML) Engineer to join our Economist Group engineering team. Your primary focus will be to design, develop, ship, maintain and oversee the overall architecture of our Economist.com data platforms - with a focus on systems related to our customer facing products. The initial focus will be around implementing appropriate data infrastructure required to enable predictive analytics and ML algorithms, and in conjunction with the Head of AI and Data Engineering support the organisation to implement their vision. The team will also support the Economist Intelligence Unit (EIU) in similar work related to forecasting technology. You'll also work with individuals from our corporate data team (responsible for reporting, analytics and insights), as well as work collaboratively with data scientists and analysts in other teams across the organisation.
The role will suit either an existing senior or a mid-level ML Engineer ready for their next challenge, but with at least some experience leading or mentoring a high performing team. You should enjoy working with autonomy in a creative and entrepreneurial environment, and have a strong commitment to producing high quality solutions. We offer flexible working and have recently shifted to a 'remote first' working policy with a minimum expectation of coming to the office two days a month, however you can come in more often if you wish to.
How you will contribute in this role:
- Architecting, building and maintaining ML processing pipelines / workflows in Python via technologies such as Spark, serverless functions, Airflow, MLflow, or other.
- Implement MLOps and related DevOps focused on creating ML workflows for deploying models for large scale inference and optimisations.
- Build Docker containers, in collaboration with Data Scientists, to support ML models for training and inference.
- Build batch and real time ML pipelines for TEG's business requirements, such as recommendation, customer lifetime value and propensity solutions.
- Support, optimise and transition our current processes to ensure well architected implementations and best practices.
- Work in an agile environment within a collaborative agile product team using Kanban
- Collaborate across departments and work closely with data science teams and with business (economists / data) analysts in refining their data requirements for various initiatives and data consumption requirements.
- Educate and train colleagues such as data scientists, analysts, and stakeholders in ML pipelining and data processing techniques, which make it easier for them to integrate and consume the data they need for their own use cases.
- Participate in ensuring compliance and governance during ML model use, to ensure that the users and consumers use the resources provisioned to them responsibly through data governance and compliance initiatives.
- Become an data and ML evangelist, and promote the available ML capabilities and expertise to business unit leaders, and educate them in leveraging these.
What we're looking for:
- 6+ years of professional experience with ML operations used in large scale digital applications.
- Extensive experience with programming in Python, Jupyter notebooks, Spark, and Docker
- Professional experience of designing, building and managing bespoke MLOps in cloud environments, using tools such as SageMaker Processing, SageMaker Pipelines, Apache Airflow, and MLflow.
- Strong, fundamental technical expertise in cloud-native technologies, such as serverless functions, API gateway, container registry, Cloud Formation / CDK.
- Experience in leading / mentoring ML engineering teams.
- Experience in working in teams with data scientists and data engineers, for building automated pipelines for model building, deployment and monitoring.
- An advanced degree in computer science, information science, data science, or a related quantitative field or equivalent work experience.
- Strong verbal and written communication skills and ability to work well with a wide range of stakeholders.
- Strong ownership, scrappy and biassed for action.
Section 4 - What we offer
Benefits
We offer excellent benefits including an incentive scheme, generous annual and parental leave policies, volunteering days and well-being support throughout the year, as well as free access to all Economist content. Country specific benefits are also offered.
Our Values
Our values are a collective set of beliefs and behaviours that strengthen The Economist Group's purpose and demonstrate where we want to be as an organisation. They reflect on our mission to pursue progress for individuals, organisations and the world.
Independence
We are not bound to any party or interest and encourage exploration and free-thinking. We champion freedom, both within our organisation and around the world.
Integrity
We are bold in our efforts to uncover the truth and stand up for what we believe in. We inspire trust through our rigour, fact-checking and transparency.
Excellence
We aspire to the highest standards in all we do. We are ambitious and inquisitive in our pursuit of continuous progress and innovation.
Inclusivity
We value diversity in thought and background and encourage healthy debate with a breadth of perspectives. We treat our colleagues and customers fairly and respectfully.
Openness
We foster a collaborative and empathetic culture conducive to the interests, wit and initiative of our colleagues. New ideas are our lifeblood.
The Economist Group values diversity. We are committed to equal opportunities and creating an inclusive environment for all our colleagues and potential colleagues regardless of ethnic origin, national origin, gender, gender identity, race, colour, religious beliefs, disability, sexual orientation, age, marital status or any other status.
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