Data Scientist / Machine Learning Engineer

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 involved with the whole system development life cycle, from brainstorming initial ideas, through prototyping to implementing production-level code and deployment. The work involves handling billions of data points per day.

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 machine learning systems and applying them 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.)

You will be part of a group consisting of 17 data scientists and data engineers under the guidance of a skilled Chief Data Scientist. (https://www.linkedin.com/in/leonardnewnham/). If you want to know what it is really like working in the Data Science team then check out our reviews on GlassDoor: https://www.glassdoor.com/Reviews/LoopMe-Data-Scientist-Reviews-EI_IE911164.0,6_KO7,21.htm Search “Data Scientist”. We have a solid 5-star rating and great feedback. To understand our values and how we like to work, check out the “About the Data Science Team” section below.

This is 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 AI space.

Key Responsibilities:

We work in small, highly interactive teams using two-week sprints. 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.

You will be working in a small team of 3-4 data scientists/machine learning engineers. You will be designing, building and running various pipelines that read terabytes of data and efficiently process it using several different modelling techniques, including some of our own novel algorithms. These pipelines will produce outputs that feed into real-time decision systems.

Some of the technologies we use include Google Cloud, Python, R, Docker, Kafka, Spark, Airflow, ElasticSearch, ClickHouse and various supervised learning algorithms.

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 of commercial experience in Python
• Experience developing machine learning pipelines that read and process large amounts of data

Bonus Qualifications:

• Experience in real-time bidding auctions
• Experience in scrum / agile software development
• Practical knowledge of infrastructure for running high-availability systems. (ElasticSearch, Kafka, ClickHouse, etc.)
• Experience of Airflow

About you:

• Excellent communication skills - you will be working with colleagues in the UK and other locations
• Has an enquiring mind and a disciplined scientific approach to extracting facts and understanding observed behaviour
• Want to be part of a fast-growing 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 are 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, profitable, high-growth company

About the Data Science Team:

• We are a team of 17 machine learning engineers, data scientists and data engineers building systems to apply the latest AI methods and research to real-world problems. LoopMe has over 300 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 supportive team in which people enjoy working with their colleagues and feel valued.
• Our values are: open communication, continual innovation (fail fast, not afraid to try things), all ideas welcome, we treat people fairly and we expect the best.
• We are doing genuinely novel and cutting-edge work. We occasionally publish papers which 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 the 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.