Data Science Engineer

Job Description

Role Overview:As a Data Science Engineer Machine Learning at Accelya, you will be responsible for building data-driven models and analytics capabilities that support decision-making within FLX ONE Cargo (F1C). Your main focus will be on developing predictive models, analytics, and data-driven services that are deployable, observable, and integrated into production systems. You will collaborate closely with AI Engineers and software teams to ensure the smooth integration of data science models into the AI system, contributing to a scalable and maintainable AI-first platform.Key Responsibilities:- Design, implement, and validate machine learning and statistical models to support pricing, capacity planning, forecasting, and operational decisions.- Translate business and operational questions into measurable data problems.- Continuously evaluate and improve model performance using real-world data.- Work with engineers to integrate data science models into production services via clean APIs.- Ensure models are versioned, testable, and suitable for cloud-native deployment.- Support incremental rollout and safe adoption of data-driven capabilities.- Work with structured and semi-structured datasets from multiple internal and external sources.- Ensure data pipelines, features, and datasets are reliable, traceable, and well-understood.- Contribute to data quality checks and monitoring.- Define evaluation frameworks, experiments, and success metrics for models and analytics.- Support A/B testing or controlled rollouts where appropriate.- Communicate results and trade-offs clearly to technical and non-technical stakeholders.- Collaborate closely with AI Engineers to align modelling approaches with AI-enabled capabilities.- Partner with Product and Engineering to ensure insights translate into real platform value.- Participate in reviews and discussions that shape how data is used across Cargo.Qualifications Required:- 35 years of experience in data science, machine learning, or data-focused engineering roles, with proven experience taking models into production.- Strong proficiency in Python and common data science libraries (e.g. pandas, scikit-learn, NumPy).- Experience building predictive or analytical models and taking them into production.- Working knowledge of SQL and experience working with cloud-based data platforms, preferably on AWS (S3, Athena, Redshift, etc.).- Strong analytical thinking and ability to explain complex results clearly.Additional Company Details:Accelya is a global leader in airline software, supporting over 200 airlines worldwide with an open, modular platform that enables modern airline retailing. The FLX ONE platform supports airlines across Offer, Order, Settlement, and Delivery (OOSD), aligned with IATA standards for modern retailing. Built on a cloud-native AWS infrastructure and backed by more than 40 years of airline industry expertise, Accelya helps airlines modernise operations, improve customer experience, and scale with confidence. Role Overview:As a Data Science Engineer Machine Learning at Accelya, you will be responsible for building data-driven models and analytics capabilities that support decision-making within FLX ONE Cargo (F1C). Your main focus will be on developing predictive models, analytics, and data-driven services that are deployable, observable, and integrated into production systems. You will collaborate closely with AI Engineers and software teams to ensure the smooth integration of data science models into the AI system, contributing to a scalable and maintainable AI-first platform.Key Responsibilities:- Design, implement, and validate machine learning and statistical models to support pricing, capacity planning, forecasting, and operational decisions.- Translate business and operational questions into measurable data problems.- Continuously evaluate and improve model performance using real-world data.- Work with engineers to integrate data science models into production services via clean APIs.- Ensure models are versioned, testable, and suitable for cloud-native deployment.- Support incremental rollout and safe adoption of data-driven capabilities.- Work with structured and semi-structured datasets from multiple internal and external sources.- Ensure data pipelines, features, and datasets are reliable, traceable, and well-understood.- Contribute to data quality checks and monitoring.- Define evaluation frameworks, experiments, and success metrics for models and analytics.- Support A/B testing or controlled rollouts where appropriate.- Communicate results and trade-offs clearly to technical and non-technical stakeholders.- Collaborate closely with AI Engineers to align modelling approaches with AI-enabled capabilities.- Partner with Product and Engineering to ensure insights translate into real platform value.- Participate in reviews and discussions that shape how data is used across Cargo.Qualifications Req

Keyskills :
PythonMachine LearningData ScienceSQLAWS