Knowledge and ability to identify patterns in data. Ability to use statistics, operations research, and other mathematical tools to make sense of information generated or collected by organisations.
Level 1 (Follow)
- Appreciation of custom coding requirement to customise every step of the data science/analytics life cycle.
- Awareness of the mainstream programming languages available (e.g., R, Python, etc).
- Awareness and understanding of all stages of data science/analytics life cycle and specifics of the data science/analytics project management.
Level 2 (Assist)
- Familiarity with the wide range of mainstream commercial and open-source data science/analytics software tools, their constraints, advantages, disadvantages and areas of application.
- Intermediate skills in using at least one such tool.
- Familiarity with programming languages (e.g., R, Python, etc).
- Basic programming skills.
- Interpret an existing script of moderate complexity.
- General understanding of all stages of Data Science/Analytics life cycle and project management.
- Assist in the scoping, planning and delivery of projects under the direction of Senior Analyst or Lead Analyst, including documenting business requirements.
- Manage moderate-scale projects and assist in management of large-scale or multi-stage projects.
Level 3 (Apply)
- Familiarity with the wide range of data science/analytics commercial and open-source software tools, their constraints, advantages, disadvantages, areas of application and mainstream packages relevant to technical stages of data science/analytics projects.
- Expertise with at least one such tool from intermediate to advanced skills in programming languages used for data science/analytics (e.g., R, Python, etc) and ability to apply these for data acquisition, pre-processing, modelling and model deployment.
- Interpret and modify existing scripts and conduct quality checks.
- Conduct general impact analysis on database change management.
- Prepare a project plan, communicate the plan to the team and allocate the tasks.
- Experience in working with stakeholders and the collection of business requirements for data science projects including establishing the business need, key stakeholders, scope, resourcing and success criteria for a specific issue.
Level 4 (Ensure)
- Familiarity with the wide range of data science/analytics commercial and open-source software tools, their constraints, advantages, disadvantages, areas of application and best-practice packages.
- In-depth expertise with at least one or two such tools.
- Advanced skills in programming languages used for data science/ analytics (e.g., R, Python, etc).
- Apply these skills for data acquisition, pre-processing, modelling and model deployment.
- Ability to coordinate quality checks of scripts for one or more projects as well as to maintain and monitor a library of team scripts and coordinate its review and updates.
Level 5 (Strategise)
- In-depth knowledge of big data technologies, the specifics of integrating them with existing information systems and using them for data science/analytics solutions.
- Design and lead data science/analytics projects including creation of a big data environment by setting up and deploying tools, capturing and evaluating results and deploying big data solutions on large-scale data sets in the enterprise.
- Lead a team in identifying a big data problem, selecting the adequate techniques and performing data acquisition, data audit, cleansing, pre-processing, model development and testing and deployment.
- Design and implement a multi-stage solution that encapsulates systems dealing with both structured and unstructured data.
- Share knowledge, experience and skills with team members through coaching and mentoring.
- In-depth understanding of all stages of data science/analytics life cycle and specifics of data science/analytics project management, the relevant resources, time requirements, etc.
- Manage large-scale data science/analytics projects and assist in managing data science/analytics programmes.