Data Scientist

Planet Paymet; Galway; Hybrid; EUR 70,000 - 110,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Planet is seeking a Data Scientist to join its AI team, working closely with Sales, Marketing, Customer Success, and Finance to turn customer and revenue data into actionable models and insights that move the top line. This hands-on role balances rigorous modelling with pragmatic delivery and end-to-end ownership.

You will build churn models, develop propensity scores for cross-sell opportunities, design attribution frameworks, and deploy models to production while communicating findings clearly

Qualifications

• 5+ years in applied data science, with commercial focus (churn, LTV, propensity, attribution)
• Strong SQL and Python (pandas, scikit-learn, statsmodels)
• Comfortable building production-grade code, not just notebooks
• Experience with cloud data warehouses (Snowflake or similar) and analytics tooling
• Experience deploying models on AWS (SageMaker, Lambda, ECS)
• Excellent communication and ability to explain models to non-technical audiences

Responsibilities

• Lead churn modelling, predict cohorts, and support retention playbooks.
• Develop propensity and next-best-action models for cross-sell opportunities.
• Design revenue attribution frameworks across marketing, sales, and product touchpoints.
• Plan and analyse A/B tests and quasi-experiments with statistical rigour.
• Take models from notebook to production with full lifecycle ownership.
• Translate business questions into analytical problems and communicate insights to stakeholders.

Skills

SQL

Python

Production-grade code

Statistics & causal inference

Data storytelling

Tools

Snowflake

BigQuery

Redshift

Databricks

AWS SageMaker

AWS Lambda

AWS ECS

Job description

About Planet Planet is a global provider of integrated technology and payments solutions for retail and hospitality customers. We create great experiences for the millions of people who use our payments, software, and tax-free solutions every minute of every day. Planet empowers its customers to deliver great customer experiences by combining payments and software in ways that drive greater loyalty, increase revenue and save time. Founded over 35 years ago and with our headquarters in London, today we have more than 2,500 employees located across six continents serving our customers in more than 120 markets.

Role overview

We're hiring a Data Scientist to join Planet's AI team and lead our commercial data science work. You'll partner with Sales, Marketing, Customer Success, and Finance to turn customer and revenue data into models and insights that directly move the top line — reducing churn, surfacing cross-sell opportunities, and giving the business a clear view of what's driving revenue. This is a hands-on, high-impact role for someone who enjoys working close to the business, owns problems end-to-end, and can balance rigorous modelling with pragmatic delivery.

What you will do

• Churn modelling — Build, deploy, and iterate on customer churn prediction models. Identify leading indicators, segment at-risk cohorts, and work with Customer Success on retention playbooks.
• Cross-sell & upsell — Develop propensity models and next-best-action recommendations to help commercial teams prioritise accounts and product conversations.
• Revenue attribution & tracking — Design attribution frameworks across marketing channels, sales motions, and product touchpoints. Build the data products that let leadership see what's actually driving revenue.
• Experimentation — Design and analyse A/B tests and quasi-experiments for commercial initiatives. Bring statistical rigour to business decisions.
• Productionisation — Take models from notebook to production. Own the full lifecycle: feature engineering, training, deployment, monitoring, retraining.
• Stakeholder partnership — Translate ambiguous commercial questions into well-scoped analytical problems. Communicate findings clearly to non-technical audiences.

Who you are

• 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 — you know when a correlation isn't enough.
• Track record of shipping models that are actually used by the business, not just built.
• Excellent communication — you can explain a model to a CFO and debug a feature pipeline with an engineer in the same afternoon.

Nice‑to‑have Experience

Experience with n8n or other workflow automation tools (Airflow, Prefect, Dagster). Exposure to payments, fintech, SaaS, or other recurring‑revenue businesses. DBT experience for analytics engineering. Experience integrating LLMs or GenAI into commercial workflows.

Why Planet

Planet is an equal opportunity employer where diversity is valued, and all employment is decided based on qualifications, merit, and business need. Come and grow your career in the most exciting, fast paced technology market, with a business that delivers feel‑good connected commerce. At Planet, we embrace a hybrid work model, with three days a week in the office. Reasonable accommodations may be made in order to allow for an individual to perform the essential functions of this role successfully.

Company Background

Planet provides integrated software, payment and technology solutions for its customers in the Hospitality and Retail sectors and worldwide via a network of global Financial Services Partners. Founded over 35 years ago, we have evolved our services, delivering an innovative digital commerce platform that puts customer experience first. With headquarters in London and around 3,000 expert employees located across six continents we serve customers in over 120 markets.