Our Purpose

We work to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. We cultivate a culture of inclusion for all employees that respects their individual strengths, views, and experiences. We believe that our differences enable us to be a better team – one that makes better decisions, drives innovation and delivers better business results.

Title and Summary

Lead MLOps Engineer

Lead MLOps Engineer

Overview

We are looking for a lead MLOps engineer to join an award winning team with a proven track record of combining data science techniques with an intimate knowledge of payments data to aid Financial Institutions in their fight against money laundering and fraud. Headquartered in The City of London, we craft bespoke services that help our clients gain an understanding of the underlying criminal behaviour that drives financial crime, empowering them to take action.

As part of the application development team, your role will focus on creating and maintaining products across the whole lifecycle, with a focus on the role of ML.

Role

• The team is experienced in building applications around ML models, this is the first MLOps specific role, to specialise as we scale.
• The focus will be on implement a full ML workflow from scratch, following Mastercard guidelines and using approved tools.
• The first phase of work will focus on model deployment, implementing CI/CD, owning the release pipeline, and the evaluation and testing out model inputs and outputs.
• You will also own model monitoring, using out existing ELK stack and grafana, creating and maintain dashboards, as well as designing services to monitor feature creep and model entropy.
• You will take part data preparation and exploratory data analysis, automating where possible.
• Bridge the gap between Data Scientists and Software Engineers.
• Work closely with Data Scientists to ensure that they understand requirements and limitations of applications and technologies.
• Work with Software Engineers to ensure that they understand feature and model requirements.
• Ensure that model training and production environments are fully reconciled.
• Assist in the evaluation of new technology choices.
• The role may include work on feature definition and model training.
• Assist in task planning and review as part of a sprint based workflow.
• Report status and manage risks within your primary application/service.
• Perform demos and join acceptance discussions with analysts, developers and product owners.

All About You

First and foremost, you enjoy building robust, smooth running services to solve real, pressing problems for your customers using ML. You enjoy working in a team, and have experience in combining best practices from DS, SE, DE and DevOps all to enable successful deployment and management of machine learning models in prod environments.

You are a generalist with knowledge and experience of ML, DevOps and Data Engineering. You have a desire to continually improve your own skills and the ability to transfer those skills to others. You look for efficiencies, optimizations, and ways to improve through automation.

• You have demonstrable experience in software development, data science, devops, operations or a related discipline.
• You are comfortable communicating with a range of stakeholders, including subject matter experts, data scientists, software engineers and enterprise devops and security professionals.
• You are a confident software developer and can write (or are happy to learn) Python and Go.
• You have a firm grasp of traditional data warehousing, can write SQL, and can optimise the use of a large relational database, as well as experience with NoSQL.
• You have experience with, and are interested in, contemporary approaches to service design, including the use of containers and container orchestration technologies, streaming data platforms, APIs and in-memory/NoSQL stores.
• You understand how to build and operate deployment pipelines.
• You can debug products end to end through logs and error messages.
• You have implemented an MLOps workflow.
• You have experience with monitoring models and creating actions from feature creep and model entropy.
• Experience of working within Agile frameworks including Scrum and Kanban
• Experience with Git version control and branching strategies, eg. trunk-based development, gitflow
• You can write clear and concise documentation designed to be used by other devops, engineering and data science teams.
• Experience with large volumes of data and high throughput, low latency solutions.
• You have experience of optimising solution performance with a constrained set of technologies.

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• Abide by Mastercard’s security policies and practices;

• Ensure the confidentiality and integrity of the information being accessed;

• Report any suspected information security violation or breach, and

• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.