WIXLIB

INTRODUCTIONThe competency descriptions should be read incorrelation with the below general maturity levelinformation. The job characteristics at each level offeradditional information about the level of autonomy,influence, accountability and complexity ks under general direction. Uses somediscretion in in less complex tasks. Workreviewed at frequent milestones.Works under general direction within aframework of accountability. Plans own workto meet given objectives and processes.Establishes team objectives andassigns responsibilities.Strategy and policy formation andapplication on the organisational level.InfluenceInteracts with and influences colleagues.Responsible for components of projects.Some responsibility for the work of others andallocation of resources. Influence the successof projects and team objectives.Influences policy and strategy formation.Initiates relationships with internal andexternal partners at senior management level.Makes decisions critical to organisationalsuccess. Inspires and influences theorganisation, and the industry at executivelevels. Develops long-term strategicrelationships with customers, partners,industry leaders and government.ComplexityPerforms a variety of work with some levelof complexity.Broad range of complex technicalor professional activities. Resolvescomplex issues.Deep understanding of complex technicaland business issues. Performs highlycomplex work activities. Contributes to theimplementation of policy and strategy.Leads formulation and implementation ofpolicy and strategy. Deep understanding ofthe industry and the organisation includingimplications of emerging technologies.Leadership andAccountabilityAnalytical and systematic approach to workwithin deadlines. Effective communicationand teamwork skills. Appreciates thebusiness context.Selects applicable standards, methods, toolsand applications. Communicates fluently andcan present complex information. Facilitatescollaboration. Plans, schedules and monitorswork to meet time and quality targets. Maycoach other staff members.Authority and accountability for a significantarea of work, including technical, financial andquality aspects. Absorbs complex informationand communicates effectively at all levels.Manages and mitigates risk. Understands theimplications of new technologies and industrychanges. Demonstrates clear leadership.Understands applicable legislation and codesof practice promoting compliance. Mentorsand coaches other staff members.Authority and accountability at anorganisational level. High level strategicmanagement and leadership skills.Understands and communicates complexideas to all levels in a persuasive andconvincing manner. Has a broad anddeep business knowledge, includingemerging technology the activities ofother organisations. Assesses the impactof legislation, and actively promotescompliance. Mentors and coaches otherstaff members.JOB CHARACTERISTICS(EQUIVALENT TO APS 4&5 OR SFIA 3/4)D ATA T O D E C I S I O N S C R C(EQUIVALENT TO APS L6 OR SFIA 5)D ATA S C IE N C E C O MP E T E N C Y F R A ME W O R K(EQUIVALENT TO APS EL1 OR SFIA 6)(EQUIVALENT TO APS EL2 OR SFIA 7)COMMERCIAL IN CONFIDENCE06

DATA SCIENCE/ANALYTICS SOLUTION LIFE-CYCLE COMPETENCIESBUSINESS UNDERSTANDING – PROBLEM IDENTIFICATIONAbility to establish the problem and whether it can be solved by analytics.AWARENESSPRACTITIONERSENIORLEADBeing aware of what kind of businessproblems can be addressed bydata-driven solutions.Utilises communication andcollaboration skills to gain anunderstanding of the organisationalproblem from stakeholders.Utilises sound communicationand collaboration skills to gain anunderstanding of the organisationalproblem from stakeholders.Utilises significant communicationand collaboration skills to gainan understanding of complexorganisational problems, frommultiple stakeholders.Utilises general knowledge of datascience/analytics and experience ofuse cases and solutions to identifywhen an issue may be amenable to adata-driven solution and to assist withsetting data science/analytics goalsand deliverables.Establishes whether the problem isamenable to a data driven solutionusing experience and knowledgeof a variety of use cases andpotential solutions.Understanding the principles andgeneral ideas of creating a datascience/analytics solution.Ability to set data science/analyticsgoals and deliverables based onthe established success criteriaand to define key metrics of thesolution’s success.Knowledge of a variety of datascience /analytics use cases thatoutline solutions to large-scalebusiness issues.Experience in designing complicated,multi-staged data-driven solutionsto real-world problems includingcollecting business requirements anddetermining if the issue is amenableto a data-driven solution or finding thekey components that are amenableto that.Ability to set data science/analyticsgoals and deliverables for complexprojects and to identify keysuccess metrics.D ATA T O D E C I S I O N S C R CD ATA S C IE N C E C O MP E T E N C Y F R A ME W O R KCOMMERCIAL IN CONFIDENCE07

DATA SCIENCE/ANALYTICS SOLUTION LIFE-CYCLE COMPETENCIESBUSINESS UNDERSTANDING – BUSINESS AND DATA UNDERSTANDINGAbility to utilise understanding of the orgnisation and it’s dataAWARENESSPRACTITIONERSENIORLEADAwareness of organisational datasources and the implications of utilisingdata to inform organisational strategy,decision making and service delivery.General understanding of theorganisational context, direction andkey strategies.Understands the organisationalcontext, functions and keystrategies in-depth.Extensive understanding of thecomplexities of the organisationcontext, functions and strategy.Understanding of a specific domain,the data applicable to the domainand the meaning of the data and itsimplications for the organisation anddecision making.Ability to apply detailed understandingof a specific domain, the dataapplicable to the domain andthe meaning of the data for theorganisation to enhance success,improve decision making anddeliver insights.Utilises organisational understandingand detailed knowledge of a range ofdata sets across a variety of domainsto create insights impacting strategy,decision making and service delivery.D ATA T O D E C I S I O N S C R CD ATA S C IE N C E C O MP E T E N C Y F R A ME W O R KCOMMERCIAL IN CONFIDENCE08

DATA SCIENCE/ANALYTICS SOLUTION LIFE-CYCLE COMPETENCIESDATA UNDERSTANDING – DATA SOURCES IDENTIFICATIONAbility to establish the availability and accessibility of data.AWARENESSPRACTITIONERSENIORLEADAppreciate data requirements andissues of availability and accessibility.Appreciate the role of data wrangling/hacking in data science/analyticsAbility to assist in establishing the keyrequired internal and external datasources as well as data availabilityand accessibility.Ability to establish the keyrequired internal and externaldata sources as well as dataavailability and accessibility formoderate-scale projects.Ability to lead the team to establishthe key required internal andexternal data sources as well asdata availability and accessibility forlarge-scale projects.Utilises in-depth knowledge of a rangeof data sources including how theyare collected, where and how they arestored, and interrelationships, bothwithin and external to the organisation,to verify relevance of potentialdata sources.Utilises extensive knowledge of arange of data sources both internaland external to the organisation,including how they are collected,where and how they are stored, andtheir interrelationships, to verifyrelevance of potential data sources.Utilises knowledge of data sourcesincluding how they are collected,where and how they are stored, withinand external to the organisation,to verify relevance of potentialdata sources.D ATA T O D E C I S I O N S C R CD ATA S C IE N C E C O MP E T E N C Y F R A ME W O R KCOMMERCIAL IN CONFIDENCE09

DATA SCIENCE/ANALYTICS SOLUTION LIFE-CYCLE COMPETENCIESDATA UNDERSTANDING – DATA ACQUISITIONAbility to acquire and wrangle data.AWARENESSPRACTITIONERSENIORLEADAwareness of privacy and ethicalimplications of sourcing internaland external dataAbility to conduct data acquisition fromrelational databases and flat files.Uses experience and knowledgeof appropriate techniques andtheir strengths, such as ETL batchprocessing, streaming ingestion,scrapers, APIs and crawlers, to acquireopen source data.Applies a wide range of advanceddata wrangling techniques, such asparsing, and algorithms for complex,multi-source data and large orcomplex projects.Ability to hack/wrangle low complexitydata selecting appropriate techniques,such as parsing, or an alogorithm, tocreate a data structure relevant tothe problem.D ATA T O D E C I S I O N S C R CD ATA S C IE N C E C O MP E T E N C Y F R A ME W O R KAbility to conduct data acquisition fromrelational databases and flat files.Ability to hack/wrangle complex data,selecting appropriate techniques, suchas parsing, and algorithims into a datastructure relevant to the problem.Ability to apply advanced methodsof data acquisition for multiple typesof data sources including ETL batchprocessing, streaming ingestion,scrapers or APIs for open source data.COMMERCIAL IN CONFIDENCE10

DATA SCIENCE/ANALYTICS SOLUTION LIFE-CYCLE COMPETENCIESDATA UNDERSTANDING – DATA AUDITAbility to conduct data quality s of the necessity of data audit.Knowledge of basic data audittechniques and approaches.Experience with a range of data audittechniques and approaches.Ability to assist the team with dataquality assessment using experienceof relevant tools and programminglanguages and general understandingof the data, potential issues such asmissing values, duplicate data, andthe implications of data quality for thedata science/analytics process.Utilises experience to design, reviewand monitor optimal approachfor data quality assessment forcomplex projects and to conductdata quality verification utilising adetailed understanding of the data,potential issues, such as missingvalues, duplicates, inconsistenciesand the implications for the datascience/analytics process.Extensive and/or in-depth knowledgeof data audit techniques andapproaches and experience inapplying them in complex settings.D ATA T O D E C I S I O N S C R CD ATA S C IE N C E C O MP E T E N C Y F R A ME W O R KAbility to design, review andmonitor optimal approach for dataquality assessment for complex orlarge projects utilising extensiveknowledge of the data, potentialissues such as missing values,duplicates and inconsistent formats,and the implications for the datascience/analytics process.COMMERCIAL IN CONFIDENCE11

DATA SCIENCE/ANALYTICS SOLUTION LIFE-CYCLE COMPETENCIESDATA UNDERSTANDING – DATA CLE ANINGAbility to identify and resolve established data issues.AWARENESSPRACTITIONERSENIORLEADAwareness of the necessity ofdata cleaning and potential dataquality issues.Knowledge of some basic datacleaning techniques and approachessuch as data wrangling, batchprocessing, data mining, dataenhancement, data harmonisation anddata standardisation.Experience in utilising a numberof data cleaning techniques andapproaches for structured andunstructured data such as datawrangling, batch processing,data mining, data enhancement,data harmonisation anddata standardisation.Extensive and/or in-depth knowledgeof best-practice data cleaningtechniques and approaches fora variety of data types such asdata wrangling, batch processing,data mining, data enhancement,data harmonisation anddata standardisation.Ability to conduct data cleaning ofnoisy, incomplete data or data withestablished data quality issues usingexperience of relevant tools andprogramming languages.Extensive experience in utilisingthese for cleaning complex, large,incomplete data or data withestablished quality issues.Ability to assist the team with datacleaning of noisy and incompletedata using relevant tools andprogramming languages.Develops understanding of whydata requires cleaning, includingthe organisational context, andthe implications of this for datascience/analysis processes.D ATA T O D E C I S I O N S C R CD ATA S C IE N C E C O MP E T E N C Y F R A ME W O R KUtilises knowledge of how theinteraction of multiple data issues,such as missing data, outliers, multiplevalues and meaning of data, impactsanalysis and identifies an appropriatecleaning approach.Ability to design and implement datacleaning approach for complex dataand projects.COMMERCIAL IN CONFIDENCE12

DATA SCIENCE/ANALYTICS SOLUTION LIFE-CYCLE COMPETENCIESDATA UNDERSTANDING – EXPLORATORY DATA ANALYSISAbility to establish data specifics and ness of the necessity ofexploratory data analysis.Knowledge of basic descriptiveanalysis techniques includingdata visualisations to establishvariable distributions andinter-variable relationships.Knowledge of a variety of exploratorydata analysis methods and techniques,such as box plots, histrograms, scatterplots and Pareto charts, suitable forvarious data types.Extensive and/or in depth knowledgeof complex data explorationtechniques and methods andexperience in applying them creativelyand effectively.Ability to assist in exploratory dataanalysis tasks.Ability to conduct exploratorydata analysis activities includingdata visualisations to establishvariable distributions andinter-variable relationships formoderate-scale projects.Ability to design, lead and coordinateexploratory analysis activitiesincluding descriptive analysistechniques, identification of relevanttrends and relationships withinthe data, data visualisation andconducting missing data analysis andimputation for complex (e.g. noisy orsparse) data.Ability to conduct missing dataanalysis and imputation utilisingrelevant techniques and establishwhether data enrichment is necessaryfor achieving the project objectives.D ATA T O D E C I S I O N S C R CD ATA S C IE N C E C O MP E T E N C Y F R A ME W O R KEstablishes whether data enrichmentis necessary for achieving largescale project objectives as wellas the nature and sources of therequired data.COMMERCIAL IN CONFIDENCE13

DATA SCIENCE/ANALYTICS SOLUTION LIFE-CYCLE COMPETENCIESPREPARE AND PREPROCESS DATA FOR MODELLING – ANALY TICS SOLUTION DESIGNAbility to design data science/analytics solutions based on the business and data level awareness of a wide range ofcore data science/analytics techniques,their advantages, disadvantages andareas of application.Uses familiarity with an increasingnumber of data science/analyticstechniques, their advantages,disadvantages, assumptionsand application to identify whichtechniques may work best.Ability to design analytical modellingapproach for moderate-scale projectsor for components of large-scaleprojects utilising sound knowledge ofdata science techniques.Design and supervise the design ofData Science/Analytics solution forlarge or complex projects.Knowledge of statistical andmachine learning techniquessuch as classification, linearregression modelling, clustering anddecision trees.Understands the data, andperformance requirements for theproblem to select the best technique.Develops ability to identify the causeof errors, such as the impact ofoutliers , and works logically to identifypotential solutions.Develops knowledge of current datascience/analytics trends.D ATA T O D E C I S I O N S C R CD ATA S C IE N C E C O MP E T E N C Y F R A ME W O R KChooses the technique optimal forthe task (for example, decision trees,advanced regression techniques suchas LASSO methods, random forestsetc.) based on knowledge of the dataand the technique’s constraints,assumptions, interpretability,robustness and application.Understanding of businessrequirements and constraintsincluding potential trade-offs betweenspeed and accuracy,Maintains knowledge of datascience trends.Chooses the technique optimal forthe task (for example, decision trees,advanced regression techniquessuch as LASSO methods, randomforests etc.) based on detailedknowledge of the data and thetechnique’s constraints, assumptions,interpretability, robustness andareas of application from a widerange of best-practice DataScience/Analytics techniques.Utilises understanding oforganisational context to negotiateperformance requirementsand implementation ofbest-practice solutions.Expands knowledge of datascience/analytics trends.COMMERCIAL IN CONFIDENCE14

DATA SCIENCE/ANALYTICS SOLUTION LIFE-CYCLE COMPETENCIESPREPARE AND PREPROCESS DATA FOR MODELLING – DATA PRE-PROCESSINGPreprocess and transform the data to ensure that it is in the optimal format, layout or shape for the project purposes.AWARENESSPRACTITIONERSENIORLEADAwareness of the necessity of datapreprocessing stage, its objectives andtime and resource requirements.Ability to create required data setutilising understanding of routineproblems, data formats, applicability ofthe data to the problem and standardmodelling techniques.Ability to create required dataset utilising understanding of theorganisational problem, applicabilityof the data to the problem, data formatand a range of modelling techniques.Ability to fuse data sources usingknowledge of data pre-processingtechniques such as transformation,integration, normalisation, featureextraction, to identify and applyappropriate methods.Ability to fuse data sourcesusing knowledge of a variety ofdata pre-processing techniquessuch as transformation,integration, normalisation, featureextraction, to identify and applyappropriate methods.In-depth knowledge of and experiencein using a wide variety of morecomplex data manipulation andtransformation techniques suchas transformation, integtration,normalisation and feature extractionto fuse and reshape complex,multi-source data.D ATA T O D E C I S I O N S C R CD ATA S C IE N C E C O MP E T E N C Y F R A ME W O R KUtilises in-depth knowledge andexperience of organisational problems,data formats and data modelling tolead the team in application of datamanipulation techniques for large orcomplex projects.COMMERCIAL IN CONFIDENCE15

DATA SCIENCE/ANALYTICS SOLUTION LIFE-CYCLE COMPETENCIESCREATE, TEST AND VALIDATE MODELS– CRE ATE MODELSAbility to develop a data science/analytical model.AWARENESSPRACTITIONERSENIORLEADAwareness of the necessity of thisstage, its objectives and time andresource requirements.Knowledge of statistical methods andbest-practice advanced modellingtechniques (predictive modelling,advanced clustering, associationrules etc).Knowledge of a wide range ofstatistical methods and best-practiceadvanced modelling techniques(for example, predictive modelling,advanced clustering, text mining,social network analysis, associationrules etc).Extensive knowledge of a range ofbest-practice advanced modellingtechniques (for example, text mining,social network analysis, predictivemodelling, advanced clustering,association rules etc).Experience in using modellingtechniques to model structured,uncomplicated data.Ability to source additionalinformation, ideas and solutionsthrough a variety of sources such asresearch and relevant libraries.D ATA T O D E C I S I O N S C R CD ATA S C IE N C E C O MP E T E N C Y F R A ME W O R KExperience in experimentingwith, selecting and developingthe modelling techniques mostsuitable for the organisationalobjective, organisational context andincreasingly complex, unstructuredand multiple data sets (for exampleincluding different types of data suchas streaming data, raw text data).Significant experience in selectingand combining modelling techniquesthat are most likely to deliver maximalaccurac

the evolving Data Science and Analytics field were previously undefined. After identifying this gap, Data to Decisions CRC (D2D CRC) embarked on a project to create a generic data science competency framework. By describing the skills, knowledge, experience and personal attributes relevant to working in data science/