Engineering Manager – Machine Learning & Data Science

Engineering Manager - Data Science & Machine Learning
Location: London, UK or Berlin, Germany

Are you a Data Science or Machine Learning Manager tired of the lack of investment or buy in from your executives?
Or searching for that excitement again when building products that you can directly see tangible outcomes?

If yes, check out the below:

An industry leading org built around fairness and sustainability within adtech is building products that can handle 500 billion auctions per day. Exceeding that of any Google or Amazon programmatic ad marketplace. The ML team is building products that help improve latency and speed. At the cutting edge of Traffic Optimization they are now in territory that has little to no research or published papers on.

A globally distributed team with the bulk of the Data Science & Machine Learning org in Berlin and London. Their Executives call the ML team the "secret sauce" to their evolution.

• Salary £140-160k
• Hybrid Working environment
• Build at Scale - work on products that overshadow Google and Amazon search scales and see direct impact through visible KPI's
• Buy in from Execs - No headaches around trying to get the smallest thing approved.
• Work in an environment that believes in constantly innovating with a product mindset (iterate, test then build)
• Progression - room to grow into a senior and then director.

As the Engineering Manager you will be the right hand of the VP of ML. To succeed in this role you need the core 4 skills.

1. People Leadership: Get into the heads of your engineers understand their strengths and weaknesses, empower them, grow them, understand how changes can benefit them. You need to know how to make your team tick in sync and proven experience of doing this before.
2. Processes: They are no longer a startup, moving from scrappy to self-sufficient & organized is a big goal for this Data Science team. Getting your team processes structured and self-sufficient is a key piece of the puzzle for this hire to be successful.
3. Data Science & ML: You need to know how these models work and all the different variables that can go wrong. Models outputs are not always correlating with high accuracy. While not expecting you have Spidey like senses the data science intuition of knowing that a model’s output may be missing something is really critical and saves them a lot of money.
4. Engineering: While any ML org would love just to do R&D the team have to make money. So having successfully built and launched products in Machine learning is the final core skill for the Engineering Manager. Understand the challenges and how to get Data Science and ML models into production and the lifecycle of an ML product will help you achieve success in this role.

If you think you could thrive in this role get in touch via apply or drop me an email at