Machine Learning Engineer: Ranking & Personalization

New

JobgetherE-commerce

Based in United KingdomFull-TimeMiddle

SalaryAnnual base salary ranging from $80,000 to $120,000 USD

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Job Details

Languages: Excellent English communication skills
Experience: At least 3 years of professional experience in applied machine learning
Required Skills: PythonSQLA/B testing

Have at least 3 years of professional experience in applied machine learning.
Understand how to develop and deploy machine learning solutions in real-world environments.
Be proficient in Python and SQL.
Have experience with big data technologies and end-to-end machine learning product development.
Have built, deployed, and maintained production-grade personalization, recommendation, or ranking systems.
Have practical knowledge of designing and analyzing A/B tests.
Use experimental results to guide product and model improvements.
Experience with a major public cloud platform is an advantage.
Demonstrate analytical and problem-solving skills, including investigating complex challenges and identifying root causes.
Have excellent English communication skills and be able to explain technical concepts and business value to diverse stakeholders.
Work effectively in cross-functional teams and balance technical priorities with product and business objectives.
Be able to work autonomously, manage responsibilities independently, and take initiative.

Design, develop, deploy, and maintain scalable, real-time machine learning services for ranking and personalization.
Develop and refine recommendation, ranking, and discovery capabilities based on user behavior and preferences.
Analyze user interactions and behavioral data to build models for relevant customer experiences.
Research, evaluate, and implement personalization approaches to maximize customer value and business performance.
Formulate and validate hypotheses through experimentation and A/B testing, measuring effects on business metrics.
Collaborate with ranking specialists, engineering teams, and technical and non-technical stakeholders.
Communicate findings, technical decisions, and business outcomes to stakeholders.
Investigate problems and improve the reliability, scalability, and effectiveness of production systems.

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