Constructor

Machine Learning Engineer: Personalization (Remote)

Remote Barcelona, Spain
SQL Spark Machine Learning Python
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Description

Constructor is the only search and product discovery platform tailor-made for enterprise ecommerce where conversions matter. Constructor's AI-first solutions make it easier for shoppers to discover products they want to buy and for ecommerce teams to deliver highly personalized experiences that drive impressive results. Optimizing specifically for ecommerce metrics like revenue, conversion rate and profit, Constructor generates consistent $10M+ lifts for some of the biggest brands in ecommerce, such as Sephora, Petco, home24, Maxeda Brands, Birkenstock and The Very Group. Constructor is a U.S. based company that was founded in 2015 by Eli Finkelshteyn and Dan McCormick. For more, visit: constructor.io.

About the Team

The Personalization team, within the Machine Learning Chapter, plays a central role in implementing algorithms that optimize for business KPIs like revenue & conversions and increase user engagement via personalizing results for a given user. We focus on metrics over features, arming our ranking algorithms with powerful personalization that brings value to customers in the way they care the most about.

The team is a cross-functional team, consisting of Machine Learning, Backend, and Frontend engineers as well as  designers, all owning & collaborating on multiple projects. As a member of the Personalization team, you will be surrounded by and encouraged to use world-class analytical, engineering, and machine learning techniques on big data to shape the evolution and scale of our personalization algorithms, metrics, and explanation. The Personalization team owns personalization signals affecting product ranking of Search, Browse, Autocomplete, and Recommendation experiences.

Challenges you will tackle

The primary focus of this job is to analyze, develop, and implement personalization signals in close collaboration with other great engineers from the Personalization team and other teams in the Data Science Chapter. The job can consist of, but is not limited to:

  • Creating user segments to analyze behaviors and provide insights to customers, enabling them to utilize this data for their own purposes, such as marketing campaigns.
  • Improving the quality of our product discovery platform by developing new & improving current (ML / heuristic based) personalization signals.
  • Defining user scenarios for personalization and shaping personalization metrics to track personalization improvements offline & online (in A/B tests).
  • Collaborate with technical and non-technical business partners to develop analytical dashboards that explain the impact of Personalization algorithms to stakeholders.
  • Participate in strategic planning, brainstorming & prioritization sessions to improve personalization. 

Hard skills:

  • You have comprehensive understanding of classical machine learning techniques.
  • You excel at Python, at least one ML/DL framework, have proficiency with any variant of SQL, and feel comfortable with the big data stack like Spark.
  • You have delivered production ML systems and conducted A/B tests to validate their value.
  • You can set hypotheses on data patterns and analyze the data to validate them.
  • You strive to continuously learn and keep the track of the recent advancements in the field of AI and ML.
  • Nice to have:
    • You have comprehensive knowledge of Natural Language Processing (NLP), especially transformer-based approaches is a plus.
    • You have experience in Personalization algorithms used in ranking systems.

Soft skills:

  • You have excellent English communication skills.
  • You enjoy helping others around you grow as developers and be successful
  • You pick up new ideas and technologies quickly, love learning and talking to others about them
  • You have data-driven mindset with a passion for experimenting and using customer feedback to drive decision-making.
  • Nice to have: you have experience collaborating in cross-functional teams.

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