Senior Machine Learning Engineer / Python Developer

The Role:
Building and scaling AI software to target ads to individual users in a high-traffic environment

A full-time Machine Learning Engineer / Data Scientist position for an exceptional candidate who has a keen interest in applying AI systems at scale. You will be designing and building machine learning systems that will operate on real-world data. You will be working in the highly exciting area of real-time ad targeting. The work involves handling billions of ad-serving requests.

LoopMe has a significant investment in AI. We are interested in pushing the boundaries and staying ahead of the competition rather than reapplying tired old systems. We do this by developing genuinely new AI systems and applying it in exciting and novel ways, please see our publications in the About the Data Science Team section below. (The second won the best paper at adKDD 2021.)

This is a rare opportunity to be involved in creating an automated system to optimize thousands of real-time bidding interactions per second with online customers, and to quantify the benefits created against control groups in a live environment. It is also an opportunity to become part of a high growth, UK tech start-up and get first-hand experience into how tech start-ups operate. You will be working in an exciting and fast-paced environment in one of the most innovative companies in the present adtech space.

Key Responsibilities:

You will join a team of 17 data scientists and data engineers led by an experienced Chief Data Scientist (https://www.linkedin.com/in/leonardnewnham/). See About the Data Science Team section below to check out who we are. You will help to solve tough (but never dull) problems, such as:

- Analysis of new data streams for inclusion in our real-time ad targeting engine

- Scaling systems to handle many terabytes of data whilst still maintaining millisecond-level response times

- Working to support the following steps in the analytical process with large (multi-million record) data sets:

o Basic data cleansing and preparation

o Variable preprocessing/transformation

o Performing statistical tests

o Preparation of data sets for predictive modeling

o Robust predictive model building, validation, and application

We are a small, highly interactive team working in an Agile environment. You will need strong communication skills. You will also be expected to care deeply about the quality of your code: its clarity, documentation, and testing.

Key Skills & Experience:

- A minimum of a Bachelor’s degree in a mathematical discipline such as Computer Science, Applied Statistics, Maths, Engineering or Physics from a respected University. An MSc or PhD is a bonus

- 4 or more years’ experience of Python and good solid knowledge of R

- Experience developing machine learning pipelines that read and process large amounts of data

- Experience of Airflow

- Experience of Spark

- Experiencing line managing small team

Bonus Qualifications:

- Experience of scrum / agile software development

- Practical knowledge of infrastructure for running high-availability systems. (ElasticSearch, Kafka, ClickHouse, etc.)

About you:

- Demonstrates a high level of initiative

- Has an enquiring mind and a disciplined scientific approach to extracting facts and understanding observed behaviour

- Excellent communication skills - you will be working with colleagues in the UK and other locations

- Consistently delivers high-quality answers

- Want to be part of a high-growth start-up company with global ambitions

- We like to make work enjoyable, so a good sense of humour is required

The ideal candidate will examine data from many perspectives, be able to think out of the box, and efficiently communicate ideas and findings to technical and non-technical peers equally. A passion for new technologies and a drive to find simple and elegant ways to implement simple solutions to complex problems is key.

Benefits:

- Competitive compensation package

- Hundreds of millions of people see your work and use our products worldwide

- A transparent work environment

- Friendly team environment where learning and acquiring new skills is encouraged

- Joining a stable, high-growth company

About the Data Science Team:

- We are a team of 17 data scientists and data engineers building systems to apply the latest AI methods and research to real-world problems. LoopMe has over 200 employees, 100 of which are technical.

- We are a distributed team with offices in London, Poland, and Ukraine. We are NOT an outsourcing team, we are a truly distributed team, where everyone’s ideas are listened to.

- We are open to new ideas and actively strive to improve both our systems and our development practices. It is a team where anyone can have a real impact.

- We are an inclusive and welcoming team in which people enjoy working with their colleagues and feel valued.

- We are doing genuinely novel and cutting-edge work. We occasionally publish papers that may give a flavour of our work in automated bidding, for example:

o Gradient Boosting Censored Regression for Winning Price Prediction in Real-Time Bidding, P Paliwal, O Renov, International Conference on Database Systems for Advanced Applications 2019

o Hybrid Dual Censored Joint Learning of Reserve Prices and Bids for Upstream Auctioneers, P Paliwal, L Stavrogiannis, ADKDD 2021

(winner of best paper award)

http://papers.adkdd.org/2021/papers/adkdd21-paliwal-hybrid.pdf

About LoopMe:

LoopMe was founded with the mission of closing the loop on brand advertising. Our full-stack tech platform harnesses mobile data, using a powerful combination of attribution, Artificial Intelligence and analytics to deliver outstanding campaign performance against brand outcomes - consideration, purchase intent, foot traffic and offline sales.

The company was founded by experienced mobile advertising executives Stephen Upstone (CEO) and Marco Van de Bergh (CTO) in 2012. LoopMe has global offices in New York, London, Chicago, LA, Atlanta, Boston, Dallas, Detroit, San Francisco, Dnipro, Singapore, Beijing, Dubai and Johannesburg.