Data Scientist

Planet

. Build, deploy, and iterate on customer churn prediction models .

Posted 10/27/2026full-timeGalway • IrelandMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying customer churn prediction models, utilizing strong SQL and Python skills, and applying statistical rigor to drive business decisions. Capable of translating complex analytical problems into actionable insights while effectively communicating findings to diverse stakeholders.

Highest-signal resume keywords

Applied Data ScienceCustomer Churn PredictionSQLPython (Pandas, Scikit-Learn, Statsmodels)Model Deployment on AWS

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Customer Churn Prediction ModelsPropensity ModelsA/B TestingFeature EngineeringStatistical AnalysisCommercial Problem SolvingData Product DevelopmentCausal InferenceModel Lifecycle ManagementAttribution Frameworks

Soft Skills

Excellent Communication Skills

Tools & Technologies

SnowflakeAWS (SageMaker, Lambda, ECS)N8nAirflowDBT

Industry Keywords

FintechSaaSRecurring-Revenue BusinessesWorkflow Automation

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSBigQueryCloudPandasPythonScikit-LearnSnowflakeSQL

About the role

Key responsibilities & impact

Build, deploy, and iterate on customer churn prediction models
Identify leading indicators and segment at-risk customer cohorts
Work with Customer Success on retention playbooks
Develop propensity models and next-best-action recommendations for cross-sell and upsell opportunities
Design attribution frameworks across marketing channels, sales motions, and product touchpoints
Build data products that show leadership what is driving revenue
Design and analyse A/B tests and quasi-experiments for commercial initiatives
Apply statistical rigour to business decisions
Take models from notebook to production
Own feature engineering, training, deployment, monitoring, and retraining across the model lifecycle
Translate ambiguous commercial questions into well-scoped analytical problems
Communicate findings clearly to non-technical audiences
Partner with Sales, Marketing, Customer Success, and Finance

Requirements

What you’ll need

5+ years in applied data science, with significant experience on commercial problems (churn, LTV, propensity, attribution, pricing, or similar)
Strong SQL and Python (pandas, scikit-learn, statsmodels)
Comfortable building production-grade code, not just notebooks
Hands-on experience with Snowflake or a comparable cloud data warehouse (BigQuery, Redshift, Databricks)
Experience deploying models on AWS (SageMaker, Lambda, ECS, or similar)
Solid grounding in statistics, experimentation, and causal inference
Track record of shipping models that are actually used by the business
Excellent communication skills
Experience with n8n or other workflow automation tools (Airflow, Prefect, Dagster) preferred
Exposure to payments, fintech, SaaS, or other recurring-revenue businesses preferred
DBT experience preferred
Experience integrating LLMs or GenAI into commercial workflows preferred

Benefits

Comp & perks

Hybrid work model with three days a week in the office
Equal opportunity employer
Reasonable accommodations may be made to allow an individual to perform the essential functions of the role successfully
Career growth opportunities