Alternate Job Titles
Data Mining Engineer, Machine Learning Engineer, Data Architect, Hadoop Engineer, Data Warehouse Architect, Commercial Intelligence Manager, Competitive Intelligence Manager, Strategic Business and Technology Consultant, Manager of Market Intelligence, Director of Enterprise Strategy, Director of Global Intelligence
Job Level
Specialist
Functional Group
Data Management
Job Family
Data
Job Description
The Data Scientist/Artificial Intelligence Scientist is responsible to:
- Autonomously identify and pursue research with significant business impact, and make compelling cases for prioritization, resource allocation, and new product strategy
- Prioritize and execute in the face of ambiguity, adapt your tools to answer complicated questions, and identify the trade-offs between speed and quality of different approaches
- Plan and lead the development of new and advanced Data Analytics techniques, methodologies and analytical solutions from design, prototyping and testing
- Collaborate with specialists in data science, analytics, engineering, and economics disciplines to efficiently develop reliable and reproducible analyses at scale
- Identify and develop core data and artificial intelligence (AI) science components for the delivery of projects,
- Architect specialised database and computing environments
- Explore and visualise complex data set to provide incremental business value
- Extract and integrate data from various sources, and create advanced models and algorithms suitable for the business use case
- Conduct testing on data and AI models, interpret findings from testing, and evaluate model performance for scaling and deployment
- Develop compelling and logically structured communication materials to facilitate stakeholder buy-in
- Support the design, implementation and maintenance of data flow channels and data processing systems that support the collection, storage, batch and real time processing, and analysis of information in a scalable, repeatable, and secure manner
- Focus on defining optimal solutions to data collection, processing, and warehousing
- Design, code, and test data systems and works on implementing those into the internal infrastructure
- Collect, parse, manage and analyse large sets of data to turn information into insights accessible through multiple platforms
- Support data processes - provide the team with ad-hoc analysis, automated dashboards, and self-service reporting tools so that everyone gets a good sense of the state of the business
- Good understanding of creating and maintaining optimal data pipeline architecture
- Build and maintain organisation’s data infrastructure requirements using wide range of data sources
- Good understanding of data governance and adherence to local and global data governance policies
Critical Work Function
Evaluation and Understanding of Business Data Needs
- Build and maintain data pipeline.
- Perform complex data analysis and report outcome to various stakeholders.
- Enhance data reliability and data quality.
- Collaborate with data analyst and data scientist.
Management of Data Preparation and Modelling
- Define objectives and hypothesis for research on data and 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.
Development and Assessment of 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.
Visualisation of Data-Driven Business Value
- 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.
Entry Requirements
#1
Data Scientist
BDQF Level 6 in IT, Computer Science, Mathematics, Statistics, Management Information Systems, Software Engineering or related field with minimum 6 years experience in related field or
BDQF Level 5 in IT, Computer Science, Mathematics, Statistics, Management Information Systems, Software Engineering or related field with 10 years relevant industry experience in data science field and certification.
Skills & Competencies
Technical Skills
Soft Skills
Recommended Technical Training Courses
Fundamental of Data Science
Statistics
Programming Knowledge
Data Manipulation and Analysis
Data Visualization
Machine Learning
Deep Learning
Big Data
Software Engineering
Model Deployment
Diverse Technologies: Hadoop, Java, Python, C++, ECL, NoSQL, HBase, CouchDB, Spark, R
Visualization: Flare, HighCharts, AmCharts, D3.js, Processing, Google Visualization API, and Raphael.js
SQL
Data Visualization
Agile