Production ML Scientist: Causal Modeling & Optimization

Hitachi, Ltd.; Greater London; Hybrid; GBP 65,000 - 100,000; Full time

Job summary

GlobalLogic is seeking a Data Scientist to drive the design, development and deployment of an enterprise decisioning platform. You will own causal models, campaign recommendations and budget-constrained pipelines in production.

Role emphasizes uplift modeling, experimentation design and end-to-end MLOps. Hybrid work model and global collaboration with engineering and business stakeholders are expected.

Qualifications

• Expert-level Python for end-to-end data science and production ML.
• Advanced SQL capabilities for complex datasets.
• Hands-on production ML on Databricks with Delta Lake and Unity Catalog.
• Proficiency with Git, CI/CD and reproducible deployments using Azure DevOps.
• Deep knowledge of uplift modeling, experimentation design, and constrained optimization.
• Familiarity with interactive front ends and ML platforms like MLflow.

Responsibilities

• Lead design, experimentation and production deployment of decisioning models.
• Develop optimization frameworks balancing campaign economics and budgets.
• Build and scale production ML pipelines on Databricks with Unity Catalog.
• Enforce engineering rigor via Git workflows, PR reviews and automated tests.
• Implement MLOps standards including monitoring, logging and drift detection.
• Collaborate with engineering and business to translate analytics into rules.

Skills

Python

SQL

Statistical modeling

Experiment design

Uplift modeling

Tools

Databricks Apps

Streamlit

MLflow

Delta Lake

Unity Catalog

Job description

GlobalLogic is seeking a Data Scientist to drive the design, development and deployment of an enterprise decisioning platform. You will own causal models, campaign recommendations and budget-constrained pipelines in production.

Role emphasizes uplift modeling, experimentation design and end-to-end MLOps. Hybrid work model and global collaboration with engineering and business stakeholders are expected.