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Role Summary
A high performing individual contributor who consistently applies and enhances the analytics capabilities, best practices and processes to solve complex business issues and identify opportunities. Operates largely independently and shows ability to mentor more junior colleagues. Regularly interacts with stakeholders up to Director level. having the ability to manage an analytical project (e.g., leading a small workstream, building analytical product).
Experience
• PhD, Masters or Bachelors (pref for Mathematics or Scientific degree) with 2-4 years work experience
OR 4+ years of experience in a comparable data analytics role with relevant experience
Education
•Demonstrable experience of delivering data-driven insights and recommendations that drove change or performance improvement for stakeholders through multiple projects using different analytics techniques.
• Demonstrable advanced level experience of using R, Python or SQL for data analysis, structuring, transforming and visualising big data*
• Experience delivering analytics projects to different business areas and displaying strong business acumen
N/A
Functional/ Technical Skills
• Critical thinking and showing an inquisitive mind
• Problem solving
• Communication and influencing
• Information gathering
• Listening
• Statistics
• SQL, Python, R or similar
• Data visualisation skills for communicating results to stakeholders of different technical levels
• Business acumen
• Basic machine learning concepts / approaches
Role Expectations
• Intermediate level ability to extract data from multiple data sources independently and combine into the required datasets for model building or analytics. (SJ - Not a basic but intermediate)
•Demonstrates awareness of intermediate supervised machine learning theory and techniques and displays a willingness to learn and apply to relevant projects and/or be a valuable partner to Machine Learning Data Science to validate and scale models for optimal business value
• Understands the need to collaborate with subject matter experts and stakeholders early in the process in order to clarify the business question, enhance the feature selection process, select an output that is relevant to stakeholders.
• Ability to comprehend probability, frequentist vs Bayesian statistics, and how to apply to business problems (eg. AB testing, log regression output). Understands difference between statistically signifiant test readout vs exploratory analysis.
• Ability to apply and comprehend statistical concepts like like regression, ANOVA, AB testing etc and understands the difference between a statistically significant test readout vs exploratory analysis.
• On measurement techniques and design ;
- Uses business acumen in combination with knowledge to appropriately select the correct technique/design to answer business questions (eg. Pre/post, causal impact, multivariate, multi-armed bandit etc)
- understands and can explain the trade offs for selecting simpler vs more complex methods
- has a deep understanding of the caveats and complex approaches to using them
• Has been exposed to a variety of data modelling challenges and has displayed the ability to define and build the data appropriately (eg. linear and non-linear regression as well as using other modelling techniques such as clustering, logistic regression etc). Correctly interprets the output and iterates and improves the model.
• Provides guidance and coaching to other team members on statistical techniques
• Possesses intermediate understanding of common data models, their underlying assumptions, the types of business questions they can best answer, and what data sources would best support the model (i.e. linear and logistic regression).
• Shows iniative to learn new modelling approaches and techniques and apply those learnings to current projects. Is able to critically evaluate the benefits of one model over another.
• Has an advanced understanding of the business domain and uses this knowledge to refine the question at the heart of the modelling project, drive model design decisions (i.e. model selection, feature engineering), and provide recommendations (model improvements, business changes, AB tests, further analysis etc).
• Favors iterative delivery and works with stakeholders to refine requirements, agree on what is in scope for each step of the project, and evolve requirements based on learnings.
• Demonstrates an understanding of technical information to understand and synthesize different data sources and create basic data pipelines and workflows to support the model. Considers legal implications and seeks out clarification and solutions if applicable.
• Values reproducability and creates shareable code and documentation to share with wider team on tools like github, IEX, confluence.
• Creates clear visualizations that support the data story and deepen the audience's understanding.
• Makes good visualisation selection without supervision and favors clarity over complexity.
• Awareness of inclusive design principles and a willingness to learn more and apply learning to visualisations (i.e. color selection)
• Familiar with common charting packages used in scripting languages like R/python (e.g. ggplot, plotly) and seeks out alternatives when needed.
• Provides constructive feedback to help upskill teammates in this area.

• Builds trust and works collaboratively and transparently with stakeholders. Seeks out analytics teammates for peer reviews, brainstorming and other upskilling and improves their own skills by doing the same for both junior and senior teammates.
• With manager's support, is able to clearly articulate project goals, methodology, caveats and conclusions to technical and non technical audiences and demonstrates good understanding of how to adjust project outputs such as presentations or executive summaries based on the audience's goals and level of technical understanding.
• Ability to tell a story in a clear and consicise way and present insights rather than just data. Seeks feedback from manager/peers/stakeholder partners early on and demosntrates ability to action the feedback on current and future projects.
• Creates relevant artifacts from the project such as technical documentation, presentations, executive summaries, and shares them in appropriate forums depending on the project and audience.
• Possesses strong knowledge and understanding of the organization's processes, objectives, and challenges and their impact on the business, and is not easily deterred if there are barriers in sharing the results/effecting change.

• Has experience working with big data and understands potential challenges and solutions and can explain this to both technical and non technical partners with minimal guidance from manager / senior peers.
• Can write new / understand existing advanced SQL such as creating views and tables, use of partitions, advanced SQL functions (e.g. RANK() Over, PARTITION BY), and is familiar with different flavors of SQL (e.g. presto/hive/postgresql ), querying tools (e.g. qubole/big query/hadoop), and concepts such as Store Procedures, Cursors and Temp tables.
• Posessess intermediate level knowledge of the most important data sources to their work area and the wider business. Knows how to find information about new data sources when required and which support channels to go to in order to unblock data issues when they arise and follows through to resolution. Shares information about data issues and shares knowledge about data sources with teammates.
• Demonstrates knowledge and use of best practices for data quality checks, query cost/performance optimisation and proactively upskills in these areas. Uses this knowledge to bring together data from different sources as required.
• Writes code in a shareable, efficient way that can be translated into a data pipeline or shared with peers.
• Is able to quickly adopt and critically evaluate new querying tools and datasets and work with the relevant teams to ensure the Analytics team have what they require to do their job

• Demonstrates capability of framing a complex business problem as an analytics problem and concrete set of analytical tasks broken down into manageable chunks.
•Works with stakeholders and analytics peers to identify the right objective and propose solutions appropriate for the task and timeframe. Demonstrates iterative thinking and ability to identify next steps based on findings.
• Picks analytically valid approaches, appropriate in terms of level ofeffort, favoring iterative delivery that solves for the objective, not the ask (i.e. not just order-taking). Uses strong understanding of the business problem space to inform the design of the solution.
• Communicates regularly with stakeholders, addressing problems as they arise and suggesting and agreeing on solutions and meeting key deadlines. Values transparency.
•Having a proactive approach to resolving problems, identifying opportunities, prompting collaboration with other team members and becoming reconisably more self sufficient
•Automates repeated measurement and reporting tasks and builds scalable dashboards to cover multiple scenarios (geo, web and apps, etc.). Trains and empowers stakeholders to pull data from automated dashboards or via scheduled reports. Empowers data democracy by providing training to stakeholders on basic use of analytics tools and solutions. Coaches other team members in this area.
•Identifies and proactively reaches out to relevant domain experts and stakeholders across different parts of the business in order to deliver maximum impact in the design, execution, socialising and use of the model.

Accommodation requests

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