Senior Data Scientist: Production ML on Snowflake

Gamma; Newbury; Hybrid; GBP 90,000 - 120,000; Full time

Benefits offered by this job

25 days leave

Parental pay

Pension scheme

Private medical insurance

Share save plan

EV scheme

Cycle to Work

Job summary

Gamma is seeking a senior data scientist to design end-to-end Snowflake‑based ML solutions that transform telecommunications data into trusted models and predictions, driving retention and revenue. You will collaborate with product, engineering, marketing, finance and commercial teams to align on analytics priorities.

You will mentor the data squad, ensure secure and scalable datasets, and deploy production ML models with measurable business impact in a hybrid work environment.

Qualifications

• Delivery of models into production environments.
• Hands-on with Python and advanced SQL.
• Snowflake as primary platform.
• Experience with telecom/subscription data is desirable.
• Mentor engineers/analysts and set data standards.

Responsibilities

• Own end-to-end ML lifecycle from problem definition to deployment and monitoring.
• Define and improve gold analytics data models on Snowflake for trusted views.
• Develop and deploy ML models for telecom data (churn, LTV, segmentation, recommendations).
• Ensure datasets and ML outputs are secure, reliable, and performant.
• Collaborate with product, engineering, marketing, finance and commercial teams.
• Mentor the data squad and establish standards across the data org.

Skills

Data science

Applied ML

Python

SQL

Snowflake

Cloud data warehousing

Mentoring

Kaggle

GitHub

Telecom data

Tools

Python

SQL

Job description

Gamma is seeking a senior data scientist to design end-to-end Snowflake‑based ML solutions that transform telecommunications data into trusted models and predictions, driving retention and revenue. You will collaborate with product, engineering, marketing, finance and commercial teams to align on analytics priorities.

You will mentor the data squad, ensure secure and scalable datasets, and deploy production ML models with measurable business impact in a hybrid work environment.