Data Scientist

Role Purpose

Lead the analysis of complex datasets to support R&D initiatives and innovation. Design and implement predictive models for air traffic management operations. Transform data into actionable insights to enable evidence-based decision-making. Ensure compliance with regulatory and safety-critical constraints in concept development within the set KPIs, agreed budgets and adopted policies and procedures.

KEY ACCOUNTABILITIES & ACTIVITIES

Key Accountability Areas Key Activities

Data Analysis

• Collect, preprocess, and analyse large datasets to uncover trends, optimise ATM processes, and predict system behaviors.

• Use statistical techniques and machine learning models to derive actionable insights from structured and unstructured data.

• Design, develop, and implement predictive and prescriptive analytics models tailored for ATM operations.

• Gather, organize, and analyse industry research and trends to provide competitive intelligence that informs R&D strategies.

• Build and maintain reusable data pipelines to facilitate seamless integration and processing of large-scale datasets.

Concept Development

• Apply advanced analytics to test and validate Proof of Concept (PoC) initiatives, using data to refine and enhance R&D prototypes.

• Develop innovative data solutions that support R&D concepts progressing through different stages of the technology readiness levels (TRLs).

• Collaborate with the Knowledge & Insights team to integrate industry research and trends into analytics models, ensuring alignment with future R&D priorities.

Data Driven Decision Making

• Monitor and improve data pipelines and analytics tools for real-time or near-real-time applications in air traffic management.

• Provide insights that optimize airspace usage, reduce delays, and improve safety outcomes for aviation operations.

• Collaborate with engineers, domain experts, and stakeholders to define problems and deliver innovative data solutions.

• Translate complex data findings into actionable insights that are understandable for technical and non-technical audiences alike, fostering data-driven decision-making.

Data Governance and Compliance

• Ensure all data analysis activities comply with regulatory standards and ethical considerations, maintaining integrity and transparency.

• Incorporate safety-critical constraints into analytics models to meet aviation industry requirements and ensure operational feasibility.

• Regularly audit data models and processes to identify risks and ensure compliance with international and organizational standards.

• Support the secure management of sensitive and critical data, ensuring alignment with SANS’s data governance policies..

Policies, Processes and Procedures

• Conduct day-to-day activities while ensuring compliance to policies and procedures

• Contribute to the identification of opportunities for continuous improvement of systems, processes taking into

account leading practices, changes in business environment, cost reduction and productivity improvement