Junior Data Scientist

Working Location: Norfolk, USA

Security Clearance: NATO Secret

Language: High proficiency level in English language

Data science, data analytics and Artificial Intelligence (AI) are increasingly gaining momentum in NATO touching all military and political domains and functional areas. In response to HQ SACT s understanding of the disruptive potential of data science and AI, and recognizing the strategic value of data, the Data Science & Artificial Intelligence section, established in 2020 in the Federated Interoperability Branch, is focusing on data science and AI as cross-cutting and enabling capabilities for HQ SACT and the NATO Enterprise. The section provides a broad spectrum from strategy and policy development and support to technical delivery and implementation to HQ SACT and the NATO Enterprise. In addition to serving as the centre of gravity for HQ SACT s efforts in advancing data centricity and integrating rapidly changing technology related to data exploitation, the section has developed a substantial reputation inside NATO and is regularly invited to offer policy and technical expertise.

A Bachelor of Science degree or equivalent from a recognized university in data science, AI, applied math, statistics, physics or a related discipline, OR two years minimum professional experience in the area of data science

Proven work experience as a Data Scientist, Data Analyst or a similar role in the last two years

Experience in modern software architecture and software development related to data science, analytics and data integration, e.g. Python, SQL, R, KNIME

Experience with data science tools to support data analytics and data visualisation

Experience involving the support to data focused operations. Such experience should encompass at least 2 skills listed below:

Experience of working within a Big Data ecosystem, with tools such as Databricks, Spark etc.

Knowledge of Machine Learning/AI, statistical and mathematical analysis

Planning, executing and maintaining Data Science pipelines

Experience with agile methodologies

A Master s degree or higher at a nationally recognised/ certified university in a relevant discipline (Data Science, AI engineering).

Experience with data presentation and visualisation tools, e.g. Microsoft Power BI, Tableau, Kibana, etc.

Experience visualizing data and producing high quality graphs and reports, charts and interactive dashboards

Experience with machine learning and AI frameworks, e.g. TensorFlow, PyTorch, scikit-learn, or other machine learning frameworks

Ability to work in a cloud environment, in particular Microsoft Azure, or Amazon Web Services

Working experience in an international environment with both military and civilian elements

Understanding of the NATO organization and its functions

Experience in project management

Contribute to the development and implementation of an enabling data science and AI capability at HQ SACT and for the NATO Enterprise

Contribute to ML/AI initiatives across HQ SACT and the NATO Enterprise with suggestions for ML approaches and implementation, including the processing of training data and performance assessment of machine learning techniques on datasets

Apply existing data science techniques to new problems and datasets using specialised programming techniques to meet needs of the project and customer

Evaluate the outcomes and performance of data science models. Identify and implement opportunities to train and improve models and the data they use

Publish reports on machine learning model outputs to meet customer needs and conforming to agreed standards

Experience of NLP, ML engineering, or computer vision

Support review of data science and AI activities, processes, products or services

Work in conjunction with other data professionals, software engineers and SMEs from different functional areas, as directed

Support development and maintenance of data pipelines for structured and unstructured data

Provide subject matter expertise to (military and civilian) staff within HQ SACT or the NATO Enterprise and develop proofs of concept, as directed

Support internal and external communities that bring together DS & AI practitioners and consumers

Support development of scientific studies

Support educational efforts and training development related to data, AI or digital literacy

Remain up-to-date with new developments in data science and AI to bring innovative ideas into implementation

Support building a data-driven culture that uses analytics to generate insights, improve decision making at all levels, inform strategy and policy decisions, and improve performance

Perform additional tasks as required, related to the LABOR category