Machine Learning Engineer, AI Decisioning

Hightouch; Deutschland; Remote; EUR 65.000 - 85.000; Vollzeit

Zusammenfassung

A leading data activation company in Germany is seeking a Machine Learning Engineer to enhance its data products with intelligence capabilities. The ideal candidate will work on personalization, predictive modeling, and content generation. This role aims to automate and optimize messaging strategies using machine learning technologies, contributing significantly to customer engagement. Join us to drive impactful changes for our clients.

Aufgaben

• Help personalize messages with relevant content for each user.
• Assist in navigating and automating experiments across messaging options.
• Build models to predict user behaviors like conversion and churn.
• Utilize advances in LLMs to help generate text and creatives.
• Analyze marketing spend to identify effective efforts.

Jobbeschreibung

We’re looking to hire a machine learning engineer as we expand our data activation products to include an intelligence layer. While hundreds of companies use Hightouch today to sync data into their SaaS systems to automate and improve operations, there’s a lot of surface area we haven’t touched in helping companies figure out which customers to message, what content to put in messages, and when to send messages. A lot of this work today is done manually through intuition and guesswork, and we believe that adding machine learning could have a step function impact for our customers. Given our access to data warehouses and databases, Hightouch is well-positioned to leverage customer data in building a powerful intelligence layer.

Some of the problems we’ll be working on include:

• Personalization and Product Recommendation: Helping personalize messages with the most relevant content for each user, considering the large options available, including which products to show from catalogues.

• Automated Experimentation: Assisting companies in navigating and automating experiments across various messaging options.

• Predictive Audiences: Building models to predict user behaviors such as conversion, churn, or other desired actions.

• Content Generation: Utilizing recent advances in LLMs to help marketers generate compelling text, images, and creatives.

• Budget Optimization: Analyzing marketing spend to identify which efforts drive the most incremental conversions and have the lowest marginal CAC.