Data Science Engineer

About the Company

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.

About the Role

As a Data Science Engineer, you will focus on building data-driven intelligence that informs decisions across FLX ONE Cargo. This role sits at the intersection of data science and software engineering—designing models and analytics that are not only accurate, but deployable, observable, and useful in production. You will work closely with product, engineering, and AI teams to turn raw operational and commercial data into predictive insights and decision-support capabilities that improve cargo outcomes.

Responsibilities

- Model Development & Analytics
- 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.
- Production Integration
- 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.
- Data Engineering & Quality
- 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.
- Measurement & Validation
- 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.
- Collaboration
- 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

- 3–5 years of experience in data science, machine learning, or data-focused software engineering roles.

Required Skills

- Strong proficiency in Python and common data science libraries (e.g. pandas, scikit-learn, Num Py).
- Experience building predictive or analytical models and taking them into production.
- Working knowledge of SQL and experience working with cloud-based data stores.
- Strong analytical thinking and ability to explain complex results clearly.

Preferred Skills

- Exposure to MLOps practices, such as model packaging, versioning, deployment, monitoring, or lifecycle management in production environments.
- Experience working with cloud-native data and ML stacks on AWS (e.g. S3, Athena, Redshift, Dynamo

DB, Sage Maker, or equivalent services).
- Familiarity with experiment tracking, evaluation, and reproducibility (e.g. MLflow or similar concepts/tools).
- Experience integrating models into production systems via APIs or batch pipelines in collaboration with software engineers.
- Exposure to large-scale data processing or analytics frameworks (e.g. Spark, Databricks, or similar).
- Experience in airline, logistics, or other operationally complex, data-rich domains.