Junior Statistician (Causal Inference)

Quick Facts

• Salary: 3200–4400 SEK per month (B2B)
• Work model: Hybrid (3 days/week in Malmö office); commuting possible for the first 3 months, relocation to Sweden required afterwards

Description

Join a large-scale production search project combining traditional lexical search (Solr/Lucene) with semantic search and machine learning-based ranking. You will work with experienced statisticians and search/ML engineers to analyze ranking performance, evaluate experiments, identify statistical biases, and understand how changes affect search relevance and user behavior. The role applies academic statistics, experimental design, and causal inference to real-world production data.

Responsibilities

• Support the design and analysis of experiments (A/B testing, interleaving) to evaluate search ranking and relevance changes
• Assist in applying causal inference methods to observational search and user engagement data
• Identify confounding factors, selection bias, seasonality, and novelty effects
• Perform statistical analyses of experiment and search log data (hypothesis testing, regression, variance estimation)
• Contribute to offline evaluation activities (click models, exposure models, position-bias correction)
• Document analytical findings, assumptions, and statistical limitations
• Communicate results clearly to engineering and product teams
• Learn and apply experimentation methodologies and statistical validation practices under senior guidance

Requirements

• BS/MS degree in Statistics, Biostatistics, Econometrics, Applied Mathematics, or a related quantitative field
• Strong statistical fundamentals: hypothesis testing, regression analysis, variance estimation
• Academic knowledge of causal inference and experimental design, including potential outcomes and Directed Acyclic Graphs (DAGs)
• Understanding of bias and confounding in observational data
• Basic experience with Python or R
• Ability to analyze real-world datasets and interpret statistical results
• Strong analytical thinking and problem-solving skills
• Ability to document and communicate analytical results clearly
• Eagerness to learn and receive feedback in a collaborative environment

Benefits

• Apply academic knowledge of statistics, experimental design, and causal inference to real production data
• Direct collaboration with international, cross-functional engineering and product teams
• Work on large-scale mapping and location-based technologies used worldwide