Data & ML Engineer

About the role

At Camlin, we develop technologies that help customers improve operational performance through asset monitoring, asset optimization, and predictive maintenance.

As a Data & ML Engineer, you'll help turn complex industrial data and domain knowledge into valuable product capabilities. Working closely with engineers, product teams, and domain experts, you'll combine data engineering, statistical modeling, machine learning, and practical implementation to solve real-world problems.

You'll have the opportunity to explore challenges, identify promising approaches, and turn them into effective, reliable solutions. We're looking for someone who combines analytical thinking with an engineering mindset, takes a pragmatic approach to problem-solving, and focuses on creating tangible value for our customers.

Responsibilities

• Translate product and engineering challenges into practical, data-driven solutions in collaboration with relevant stakeholders.

• Explore and analyze complex datasets to identify patterns, anomalies, and opportunities for improvement.

• Develop and evaluate statistical models and machine learning approaches for applications such as asset monitoring, predictive maintenance, forecasting, and optimization.

• Build and improve data pipelines, transformations, and supporting tooling to enable reliable data-driven capabilities.

• Turn analytical findings and modeling approaches into practical, maintainable implementations that can be integrated into our products.

• Evaluate performance, investigate issues, and continuously improve solutions based on data, technical evidence, and real-world results.

• Make sound technical decisions, balancing effectiveness, complexity, maintainability, and implementation effort.

• Communicate findings and technical trade-offs clearly, working across engineering, product, and domain teams.

What you'll need

• Strong Python programming skills and proficiency in SQL.

• Practical experience with data analysis, statistical modeling, and machine learning.

• Knowledge of relevant techniques such as classification, regression, anomaly detection, forecasting, or optimization.

• Experience working with complex datasets and developing data-driven solutions.

• Solid engineering fundamentals, including Git, testing, documentation, and maintainable code.

• Understanding of data pipelines, data quality, model evaluation, and validation.

• A pragmatic, results-oriented mindset, with the ability to take ownership of problems from initial investigation through implementation.