Data Scientist: ML Engines & Production Analytics

Scmdojo; Manchester; Hybrid; GBP 65,000 - 90,000; Full time

Job summary

Anaplan is seeking a versatile Data Scientist to join the Functional Analytics team in Manchester. You will design and develop functional analytic engines that integrate into Anaplan Applications, building best-in-class ML models and scalable pipelines to deliver actionable insights at scale.

You will partner with Customer Success, Product, and Engineering, applying time-series forecasting and advanced ML techniques (LightGBM/XGBoost, PyTorch) in a fast-paced environment, with a focus on

Qualifications

• Strong hands-on experience in data science, building and deploying systems to production.
• Proven ability to design ML models and scalable pipelines.
• Solid Python code, testing, CI/CD and software best practices.
• Experience with modern data warehouses and transformation tools.

Responsibilities

• Analytic engine development: design scalable capabilities to integrate into applications.
• Develop production-grade ML models and pipelines for automated insights.
• Advanced modeling and time-series forecasting for demand sensing.
• Build data pipelines with SQL/Python (dbt, Snowflake) to extract signals.
• Lead end-to-end MLOps deployment with MLflow/Optuna/Prefect/Airflow.

Skills

Data Science

ML Engineering

Python

SQL

Snowflake

Databricks

PyTorch

MLflow

Optuna

Airflow

Cloud Computing

Time-series Forecasting

Supply Chain Forecasting

Education

MS/PhD in Computer Science or related field

Tools

Snowflake

Databricks

dbt

Python tooling

Job description

Anaplan is seeking a versatile Data Scientist to join the Functional Analytics team in Manchester. You will design and develop functional analytic engines that integrate into Anaplan Applications, building best-in-class ML models and scalable pipelines to deliver actionable insights at scale.

You will partner with Customer Success, Product, and Engineering, applying time-series forecasting and advanced ML techniques (LightGBM/XGBoost, PyTorch) in a fast-paced environment, with a focus on