Data Scientist/Artificial Intelligence Scientist
The Data Scientist/Artificial Intelligence Scientist plans and leads the development of new and advanced data analytic techniques, methodologies and analytical solutions from design, prototyping and testing. He/She identifies and develops core data and artificial intelligence (AI) science components for the delivery of projects, architects specialised database and computing environments, explores and visualises complex data set to provide incremental business value. He extracts and integrates data from various sources, and creates advanced models and algorithms suitable for the business use case. He conducts testing on data and AI models, interprets findings from testing, and evaluates model performance for scaling and deployment. He develops compelling and logically structured communication materials to facilitate stakeholder buy-in. He works in a team setting and is proficient in statistics, scripting and programming languages required by the organisation. He is also familiar with the relevant software platforms on which the solution is deployed on. The Data Scientist/AI Scientist has strong analytical and critical thinking skills to identify and solve problems. He is passionate about analysing and resolving complex business problems, displaying intellectual curiosity towards using data and AI to address business needs and challenges. He is a data storyteller, and is able to influence key stakeholders and spearhead a data driven approach to resolve business issues.
What Does a Data Scientist/Artificial Intelligence Scientist Do?
Key Responsibilities & Tasks
Manage data preparation and modelling
- Define objectives and hypothesis for research on data and artificial intelligence (AI) models
- Analyse the ways in which datasets may be biased and address this in safety measures and deployment strategies
- Conduct extraction and integration of data including features from different data sources
- Develop multiple models and algorithms suitable for the use case
- Perform model comparison to draw inferences on variable importance
- Select the best model based on pre-defined evaluation criteria
- Account for data ethics and policies in model selection and evaluation process
- Interpret and evaluate model performance for scaling and deployment
Build and assess models
- Conduct testing on final model in real-time business conditions prior to deployment
- Scale and deploy models in real-time business conditions for end user consumption
- Initiate autonomous monitoring to scale human oversight
- Document modelling techniques used and assumptions made against test outcomes
- Enable end user capability to use AI/ Data Science products effectively
Present data driven business value of data science/AI models
- Create reports and deliverables based on insights derived from the model results
- Develop compelling, logically structured presentations including story-telling of research and/or analytics findings to secure stakeholder commitment
- Contribute to the creation of leading-edge resources, including playbooks, guides, blog posts, videos, etc.
Do You Have the Skills for This Role?
A Data Scientist/Artificial Intelligence Scientist needs 3 core competencies. Here's what's required and at what level.
Must-Have Skills (Advanced)
Developing People
AdvancedInteracting with Others
Transdisciplinary Thinking
AdvancedThinking Critically
Supporting Skills
Communication
IntermediateInteracting with Others
SkillsFuture Skill Levels
3 levelsBasic
Recognise and understand fundamental concepts. Apply skills in routine situations with guidance.
Intermediate
Apply skills in varied situations independently. Analyse problems and adapt approaches as needed.
Advanced
Lead and innovate in complex situations. Evaluate strategies, guide teams, and drive improvements.
Technical Skills & Competencies (TSC) Levels
6 levelsFollow
Carry out routine tasks under close supervision. Follow established procedures and guidelines.
Assist
Perform tasks with some independence. Assist in non-routine situations and apply established techniques.
Apply
Apply skills and knowledge independently in varied situations. Analyse problems and adapt approaches.
Analyse
Analyse complex situations and develop solutions. Guide and mentor junior colleagues.
Strategise
Set strategic direction and drive innovation. Evaluate trade-offs and make high-impact decisions.
Transform
Lead industry transformation. Establish standards, shape policy, and provide expert advisory.
Technical Skills & Competencies
A Data Scientist/Artificial Intelligence Scientist requires 19 technical skills at specific proficiency levels.
Text Analytics and Processing
Level 6Development and Implementation
Design and implement systems that can interact with users using spoken or written natural language
Business Innovation
Level 5Business and Project Management
Prioritise business innovation opportunities and design digital architectures and processes to facilitate the creation of an innovative business environment
Business Needs Analysis
Level 5Business and Project Management
Lead comprehensive analysis to understand underlying drivers and present a compelling business case for proposed IT solutions
Computational Modelling
Level 5Development and Implementation
Design advanced statistical and computational models, and spearhead the application of algorithms and modelling techniques to new domains
Data Design
Level 5Design and Architecture
Establish a strategy for the creation of large-scale data models and structures and spearhead the implementation of database technology, architectures, software and facilities
Data Ethics
Level 5Governance and Compliance
Formulate the organisation’s code of ethics, systems and processes to ensure adherence to professional, legal and ethical requirements for data usage
Data Governance
Level 5Governance and Compliance
Develop organisation practices and standards for handling data throughout their lifecycle, resolve breaches, and oversee transfer of data between organisations
Data Strategy
Level 5Strategy Planning and Implementation
Establish data management strategies to extract maximum value from information assets and support decision-making and business processes
Design Thinking Practice
Level 5Design and Architecture
Establish effective design thinking processes, methodologies and frameworks to proliferate design thinking across the organisation
Intelligent Reasoning
Level 5Development and Implementation
Evaluate, design and build intelligent software systems
Pattern Recognition Systems
Level 5Development and Implementation
Develop intelligent systems using machine learning techniques
Project Management
Level 5Business and Project Management
Lead end-to-end management of large programmes or multiple projects concurrently, coordinating project interdependencies
Quality Standards
Level 5Governance and Compliance
Establish and control quality expectations in line with organisation directions and selected benchmarks
Solution Architecture
Level 5Design and Architecture
Establish frameworks and determine relevant tools and techniques to guide the development IT solutions
Software Design
Level 5Design and Architecture
Translate complex software ideas and concepts into a design blueprint and establish key design principles and methodologies
Test Planning
Level 5Development and Implementation
Develop a test strategy, and establish testing policies, guidelines and metrics according to internal and external standards
Computer Vision Technology
Level 4Development and Implementation
Set-up and deploy video analytics algorithms and perform system performance evaluations
Emerging Technology Synthesis
Level 4Business and Project Management
Evaluate new and emerging technology and trends against the organisational needs and processes
Stakeholder Management
Level 4Stakeholder and Contract Management
Develop a stakeholder engagement plan and negotiate with stakeholders to arrive at mutually-beneficial arrangements
European Skills Framework
ESCOSkills and knowledge areas required for this occupation based on European classification.
Essential
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