Data Scientist II, Product Analytics

Basic level ability to extract data from multiple data sources and combine into the required datasets for model building or analytics

Has a basic understanding of probability, frequentist , and how to apply to business problems (eg. AB testing, log regression output). Understands difference between statistically significant test readout vs exploratory analysis.

Demonstrates beginner level understanding and correct application of descriptive statistics. Like Analysis of variance, regression analysis & knowledge of basic probability theorem

Is able to select appropriate measurement techniques and design and recommend both simple and complex experiments to answer business questions eg. Pre/post, AB, causal impact etc.

Understands caveats and more complex approaches to common experiment types (pre/post, AB, causal impact etc).

Rudimentary understanding of and ability to build common models such as linear and non-linear regression, clustering etc and a basic understanding the format of the data required for the modelling process.

Possesses basic level 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.

Has a basic knowledge 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). Manager plays a strong support role

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 and guidance from manager

Demonstrates an understanding of technical information to understand and synthesize different data sources and create basic data pipelines and workflows to support the model.

Values reproducibility 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 established visualization selection with limited supervision and favors clarity over complexity.

Awareness of inclusive design principles and a willingness to learn more and apply learning to visualizations (i.e. color selection).

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 concise way and present insights rather than just data. Seeks feedback from manager/peers/stakeholder partners early on and demonstrates 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.

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 intermediate SQL such as CASE WHEN THEN BREAK END, subqueries, UNION, use of variables (set, declare), use of built-in functions.

Possesses beginner 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 optimization 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.

Demonstrates capability of framing a business problem as an analytics problem and concrete set of analytical tasks broken down into manageable chunks. Is notably becoming more self sufficient in the support that they require

\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 of effort, favoring iterative delivery that solves for the objective, not the ask (i.e. not just order-taking). Uses intermediate understanding of the business problem space to inform the design of the solution.

With manager support, 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 reconcilably 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