Build Skills. Gain Recognition.
Advance Your Career.

Please check you meet these required skills for this grade:

• Applies a range of foundational statistics (e.g. Regression or work on categorical analysis)
• Plans and executes a manual and automated data cleaning strategy
• Explains the benefits and disadvantages of methods for dealing with missing data (e.g. listwise deletion)
• Explains risks of data collection and data bias
• Embeds visualisations in data stories, making intentional choices and avoiding deception
• Uses a mix of automation and manual work to create new features of data
• Gets a data set ready for the machine learning pipeline
• Reports confidence intervals and risk factors for all results
• Plans, implements and reports on a strategy to normalise the data for machine learning
• Works with the output of common machine learning models (regression, clustering, categorisation, anomaly detection, tree based models and association mining)

Please check you meet these required skills for this grade:

• Confidently manipulates and gains insight from downloaded and remote data sets
• Chooses imputation methods intentionally
• Makes connections across data sets efficiently and effectively
• Carries out machine learning processes in a coded environment (R or Python)
• Works with feature engineering to improve outputs
• Demonstrates thorough understanding of deep learning frameworks and architectures either through practice or technical knowledge
• Demonstrates advanced understanding of mathematics, statistics, probability, pattern detection and algorithm construction relevant to the analytical process
• Uses SQL flexibly
• Works within emerging and sometimes complex data and AI regulatory frameworks

Please check you meet these required skills for this grade:

• Works with a range of data structures on solutions to complex problems
• Flexibly identifies environments to host solutions
• Takes a systematic approach to data pipelines
• Identifies strategies to work with new technology

Governance and Professionalism

• Sets standards in the organisation for ethical data practices
• Acts with integrity above and beyond own regulatory and legal jurisdictions
• Constructively evaluates own work and peer work
• Consistently operates within industry standards of interoperability
• Actively promotes and implements sustainable product design

Communication

• Negotiates effectively
• Creates quality visuals
• Aligns to international standards of accessibility
• Works to open science research standards
• Builds effective external business relationships

Leadership

• Reliably contributes to company risk strategy
• Identifies and critically evaluates assumptions
• Empowers others beyond the organisation
• Monitors developments
• Contributes in a significant way to the organisation

Eligibility Criteria

Fellow Applicants will need to demonstrate their significant contribution to the field of analytics or wider society, such as:

• 1. The creation of an innovative product using analytics that has transformed the business environment
• 2. The creation of an innovative company, charity or social cause using analytics
• 3. Leading on an analytics project that has positively impacted your local community, wider society, or has addressed a social or environmental challenge
• 4. Outreach in the analytics community, particularly influencing those thinking about a career in analytics or those just starting their career
• 5. A significant contribution to the field of research in analytics or data science
• 6. A significant contribution to open source code or tools, or network infrastructure
• 7. A significant contribution to the development of government policy based on evidence through analytics

You will need to demonstrate Criterion 5 (wider impact outside of carrying out your job role), plus at least one other criterion from your supporting evidence:

• 1. Has a record of solving complex and intractable problems and / or novel problems using innovative techniques.
• 2. Directs policy and leads the way in improving standards outside of own organisation.
• 3. Is a respected expert in the field evidenced through a body of work.
• 4. Influences policy and best practice beyond their own organisation.
• 5. Has made a significant contribution to the field beyond their own organisation.

2 signed letters of referral from other professionals in the field, speaking to how they know you and your work and how you meet the criteria. Please include the referee's telephone number, Linkedin profile link and professional email address. (We may contact your referees).

The Review Process & Timeline

• 1. Applications for IoA Fellowship are peer reviewed and agreed by the Fellow Peer Review Committee in review cycles during the year
• 2. The Fellow Peer Review Committee meets every 4 months to review Fellow applications with the supporting documentation that you have provided
• 3. Fellow Review Committee meetings are held virtually at the end of January, May and September and your application will be reviewed at the next panel meeting, depending when you apply during the year
• 4. Applications received between 1st December and 31st March will be reviewed at the end of May
• 5. Applications received between 1st April and 30th July will be reviewed at the end of September
• 6. Applications received between 1st of August and 30th November will be reviewed at the end of January