Scientific Computing & Spatial Analytics Engineer

SoftServe; Poland; On-site; PLN 180,000 - 260,000; Full time

Job summary

SoftServe is seeking an experienced engineer to develop scientific computing and spatial analytics solutions for a strategic platform across multiple regions. You will transform time-series and geospatial data into practical tools, collaborating with cross-functional teams to drive decisions in mining operations.

The role emphasizes Python-based data apps, Dockerized deployment, geostatistics, and Azure Container Apps with Service Bus to enable scalable analytics and MLOps practices.

Qualifications

• Strong hands-on experience with Python and numerical/data analysis libraries (NumPy, SciPy, Pandas, or Polars).
• Practical experience developing and deploying Dockerized applications.
• Working knowledge of machine learning concepts and related statistical methods.
• Proficiency in SQL for querying and managing large datasets.
• Experience with time-series analysis and spatial interpolation techniques.
• Exposure to C++ or .NET (C#) is a plus.
• Familiarity with Microsoft SQL Server, PostGIS, or geostatistics is an advantage.
• Experience with Azure Cloud services, including Container Apps and Service Bus, is a plus.

Responsibilities

• Design and build data-intensive Python applications that transform raw sensor, geological, and operational data into actionable insights.
• Develop and deploy Dockerized services to package analytical models for consistent use across global mining sites.
• Apply spatial interpolation and geostatistical methods to model resource distributions, ore bodies, and operational risk.
• Collaborate with data scientists, geologists, and platform engineers to integrate machine learning models into production workflows.
• Query, structure, and optimize large operational datasets in SQL to support reliable downstream analytics.
• Build and maintain services on Azure, using Container Apps and Service Bus to support scalable, event-driven data processing.
• Support MLOps practices to monitor, retrain, and version models as new field data becomes available.
• Share findings and technical approaches with stakeholders across regions and functions.

Skills

Python

NumPy

SciPy

Pandas

Polars

SQL

Time-series analysis

Geostatistics

Machine learning concepts

C++ or .NET (C#) knowledge

Tools

Docker

Azure Container Apps

Service Bus

PostGIS

Microsoft SQL Server

Job description

About The Role

In this role, you’ll build scientific computing and spatial analytics solutions for a strategic platform inside Anglo American plc, supporting the exploration, extraction, processing, and marketing of copper, premium iron ore, platinum group metals, and diamonds. You’ll turn time-series and geospatial data into tools that guide decisions across mining operations in South America, Africa, Australia, Europe, and North America, working alongside a cross-functional team where technical ownership and innovation drive real business outcomes.

Responsibilities

• Design and build data-intensive Python applications that transform raw sensor, geological, and operational data into actionable insights.
• Develop and deploy Dockerized services to package analytical models for consistent use across global mining sites.
• Apply spatial interpolation and geostatistical methods to model resource distributions, ore bodies, and operational risk.
• Collaborate with data scientists, geologists, and platform engineers to integrate machine learning models into production workflows.
• Query, structure, and optimize large operational datasets in SQL to support reliable downstream analytics.
• Build and maintain services on Azure, using Container Apps and Service Bus to support scalable, event-driven data processing.
• Support MLOps practices to monitor, retrain, and version models as new field data becomes available.
• Share findings and technical approaches with stakeholders across regions and functions.

Requirements

• Strong hands-on experience with Python and numerical/data analysis libraries such as NumPy, SciPy, Pandas, or Polars.
• Practical experience developing and deploying Dockerized applications.
• Working knowledge of machine learning concepts and related statistical methods.
• Proficiency in SQL for querying and managing large datasets.
• Experience with time-series analysis and spatial interpolation techniques.
• Exposure to C++ or .NET (C#) is a plus.
• Familiarity with Microsoft SQL Server, PostGIS, or geostatistics is an advantage.
• Experience with Azure Cloud services, including Container Apps and Service Bus, is a plus.
• Comfortable working independently and collaborating across distributed, cross-functional teams.

SoftServe is an equal opportunity employer. Qualified applicants will receive consideration regardless of race, color, ancestry, ethnicity, national origin, religion, sex, sexual orientation, gender identity or expression, age, citizenship, disability, health condition, marital or family status, veteran status, or any other characteristic protected by applicable law.