Principal Data Scientist / Analytics Lead job description

Data and analytics — Principal Data Scientist / Analytics Lead
Shapes the analytics and data science strategy org-wide.

RESPONSIBILITIES
- Set query and warehouse-cost standards across the organization and take on the most contested queries other teams escalate.
- Set the modeling standards for the warehouse and arbitrate between teams whose models disagree about what a shared entity means.
- Set the pipeline platform and reliability standards for the organization, and lead diagnosis of cross-system pipeline failures whose cause is not obvious.
- Set the transformation standards for the warehouse, including where logic belongs, and resolve disputes about which version of a number is correct.
- Set the warehouse architecture strategy for the organization, including platform choice, and defend it against both cost and performance pressure.
- Set the quality standards and the checks a dataset must pass before publication across the organization, and tell a modeling gap from a source-system fault.

REQUIREMENTS
- Experience at the Lead Data Scientist level (level 4 of 5) or equivalent
- Holds AWS Certified Data Engineer - Associate and Salesforce Certified Tableau Desktop Foundations

REQUIRED SKILLS
Data Engineering
- Analytical SQL Querying — You can set query and warehouse-cost standards across the organization and take on the most contested queries other teams escalate.
- Dimensional Data Modeling — You can set the modeling standards for the warehouse and arbitrate between teams whose models disagree about what a shared entity means.
- Data Pipeline Development — You can set the pipeline platform and reliability standards for the organization, and lead diagnosis of cross-system pipeline failures whose cause is not obvious.
- ETL/ELT Transformation Design — You can set the transformation standards for the warehouse, including where logic belongs, and resolve disputes about which version of a number is correct.
- Data Warehouse Architecture — You can set the warehouse architecture strategy for the organization, including platform choice, and defend it against both cost and performance pressure.

Data Governance
- Data Quality and Pipeline Monitoring — You can set the quality standards and the checks a dataset must pass before publication across the organization, and tell a modeling gap from a source-system fault.
- Metric Definition and Governance — You can own the metric governance framework for the organization and arbitrate the highest-stakes disputes about what a headline number means.
- Data Lineage and Documentation — You can untangle the lineage of a messy legacy dataset nobody has documented, and design how lineage is captured so it stays accurate as pipelines change. You get other teams to document what they own.
- Data Ethics and Privacy — You can set how the organization decides and evidences who gets access to which data, along with the ethics and privacy standards and how automated outputs are explained.

Statistics & ML
- Statistical Analysis and Inference — You can set the statistical standards the organization's analyses are held to, and are the final call when a result is genuinely disputed.
- Experiment Design and A/B Testing — You can set experimentation strategy and standards for the organization, and decide when a question genuinely cannot be answered by an A/B test.
- Predictive Modeling and Machine Learning — You can take a model, including a pre-trained AI model, to production with tests on real cases and monitoring for drift.
- Feature Engineering — You can engineer features from messy or sparse data where the obvious fields do not work, and build them so training and production see the same values. You review what others build for leakage and drift.
- Forecasting and Trend Analysis — You can forecast through a launch, promotion or structural shift, and state what the model cannot predict before the forecast drives a commitment.
- Data Exploration and Preparation — You can work out what the fields in a poorly documented dataset mean from the data itself, and build preparation that reruns to the same result.
- Programming for Analysis — You can set the coding, testing and reproducibility standards that analysis code must meet across the organization.

Visualization
- Dashboard Design — You can set the reporting and self-serve analytics strategy for the organization, and judge when a dashboard is the wrong tool for the question being asked.
- Data Visualization Best Practice — You can present awkward data, such as many series, wide ranges or heavy uncertainty, so it stays readable and accurate. You review what others publish and can say precisely what a design overstates.
- Stakeholder Analytics Translation — You can shape how leadership uses data in decisions, and tell them plainly when the data does not support their preferred answer.
- Analytics Storytelling and Reporting — You can take a difficult or unwelcome finding to senior stakeholders and keep the argument standing under challenge. You coach others to cut the detail that interests an analyst but changes no decision.

Delivery
- Project Management — You can set the everyday habits for how work is organized and followed up across the organization, and teams you do not lead adopt the routines you introduce.
- Planning & Estimation — You can forecast work and staffing quarters ahead, and set the estimating method other teams use.
- Ownership & Accountability — You can report the organization's most exposed outcomes to its leaders as they stand, including bad news, and answer for them.
- Quality Focus — You can raise the quality bar across the organization by building the tools and habits that make the careful way the easy way.

Craft
- Problem Solving — You can crack problems the organization has repeatedly failed to solve, and your approach becomes how others tackle that whole kind of problem.
- Domain Expertise — You can influence how the field is practiced beyond this organization, and your expertise settles questions that have stood open for years.
- Continuous Learning — You can set what the organization invests its learning time in, and the material and practice you create outlive your involvement.

Communication
- Communication — You can set how the organization communicates by creating the forums and norms that teams use to share information.
- Collaboration — You can remove the barriers that stop teams working together by putting in place routines and forums they use across the organization.
- Writing & Documentation — You can set the writing standard the organization works to, and documents you wrote are still in use long after you have moved on.
- Stakeholder Management — You can represent the organization in its most important relationships and set how it engages with everyone who depends on it.

Leadership
- Leadership — You can set direction for the whole organization, make hard calls under real uncertainty, and carry the decisions nobody else wants to own.
- Mentoring — You can shape how the organization grows its people, and those you developed are themselves named by others as strong in their field.
- Strategic Thinking — You can shape the organization's strategy, and the bets you argued for are visible in where it ended up.

REQUIRED CERTIFICATIONS
- Salesforce Certified Tableau Desktop Foundations (required from Data Analyst)
- AWS Certified Data Engineer - Associate (required from Senior Data Analyst)