Statistician / Causal Inference Scientist — Search Relevance & Ranking

Functional Software Stockholm AB; Malmö kommun; On-site; SEK 900,000 - 1,300,000; Full time

Job summary

Functional Software Stockholm AB in Malmö seeks a senior statistics and causal inference expert to design experiments and analyze ranking performance in a production search system.

You will work with ML and Search engineers on experimental design, causal methods, and online/offline evaluation, translating results into actionable guidance for product teams. Fluent English required; Swedish a plus.

Qualifications

• Advanced degree in quantitative field (Statistics/ Econometrics/Applied Math).
• 8+ years of development experience with a strong causal inference focus.
• Strong grounding in classical statistical modeling and uncertainty quantification.
• Experience analyzing large-scale behavioral data (search logs, clickstreams).
• Proficiency in Python or R for independent analysis, not necessarily production code.

Responsibilities

• Design experiments (A/B tests, interleaving, quasi-experiments) for ranking and relevance changes.
• Apply causal inference methods to observational data where randomization is impractical.
• Diagnose confounding, selection bias, and interference in search experiments and metrics.
• Collaborate with ML and search engineers to interpret model behavior statistically.
• Develop models of user behavior and search outcomes to support offline evaluation.
• Communicate findings and caveats clearly to engineers and product stakeholders.
• Advise on experimentation infrastructure, guardrails, and sample ratio mismatch checks.

Skills

Statistics

Causal inference

Experiment design

Programming (Python/R)

Communication

Education

MS/PhD in Statistics or related field

Tools

DoWhy

EconML

CausalImpact

Job description

Job Description

You will join a search team responsible for a large-scale production search system combining lexical retrieval (Solr/Lucene) with semantic and ML-based ranking. The environment is mature and heavily instrumented, with ranking models that have been in production and continuously refined for years.

You will join a search team responsible for a large-scale production search system combining lexical retrieval (Solr/Lucene) with semantic and ML-based ranking. The environment is mature and heavily instrumented, with ranking models that have been in production and continuously refined for years.

Your role is to bring rigorous statistical and causal reasoning to complex questions around ranking performance, experimentation and user behavior — separating causation from correlation and quantifying uncertainty in production decisions.

This is fundamentally a statistics and applied mathematics role. You will work closely with Search and ML Engineers, with a focus on experimental design, causal inference, variance reduction and statistical rigor rather than production software engineering.

Responsibilities

• Design experiments (A/B tests, interleaving, quasi-experiments) for ranking and relevance changes, including power analysis, randomization strategy, and metric selection.
• Apply causal inference methods (e.g., difference-in-differences, instrumental variables, propensity/matching methods, synthetic control, uplift modeling) to observational search and engagement data where randomized experiments are impractical.
• Diagnose confounding, selection bias, and interference (e.g., network/spillover effects between ranked results) in search experiments and metrics.
• Partner with ML and search engineers to interpret model behavior statistically — separating genuine signal from noise, seasonality, novelty effects, and measurement artifacts.
• Develop and validate statistical models of user behavior and search outcomes (e.g., click models, exposure models, position bias correction) to support offline evaluation.
• Communicate findings and their statistical caveats clearly to engineering and product stakeholders, including what conclusions are and are not supported by the data.
• Advise on experimentation infrastructure and methodology (metric definitions, guardrails, sample ratio mismatch checks) to keep the team's experimental culture rigorous over time.

Must-have Qualifications

• Advanced degree (MS/PhD or equivalent experience) in Statistics, Biostatistics, Econometrics, Applied Mathematics, or a related quantitative field.
• At least +8 years of experience in development
• Demonstrated depth in causal inference: experimental design, quasi-experimental methods, and the assumptions/limitations underlying each.
• Strong grounding in classical statistical modeling — hypothesis testing, regression, variance estimation, Bayesian methods, or related techniques — with the ability to justify model choices mathematically.
• Experience analyzing behavioral or observational data at scale (e.g., search logs, clickstream, recommendation/engagement data) and reasoning about biases specific to that data (position bias, selection bias, feedback loops).
• Comfort working with real, messy production data and communicating uncertainty honestly rather than overclaiming significance.
• Sufficient programming/scripting ability (e.g., Python, R) to conduct independent analysis — engineering-grade software skills are not required.
• Excellent written and verbal communication, including the ability to explain statistical reasoning and caveats to non-statistician engineering audiences.

Extra Merit Qualifications

• Prior experience in search, recommendation, or online marketplace settings where ranking decisions are evaluated via experimentation.
• Familiarity with information retrieval evaluation concepts (relevance judgments, ranking metrics such as NDCG/MRR) sufficient to collaborate with ML engineers, even without building such systems yourself.
• Experience with click/exposure modeling or position-bias correction methods specific to ranked
• Publication or applied track record in causal inference methodology (e.g., work with DoWhy, EconML, CausalImpact, or comparable frameworks/literature).
• Background collaborating with engineering teams without owning production code delivery.

Information

• Language Requirements: Fluent English (mandatory), Swedish
• Scope: 100%
• Location: Malmö, 3 days on site
• Start: Immediate
• Duration: 12 months, with possible extension