Principal Data Scientist job description

Data and analytics — Principal Data Scientist
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 transformation standards for the warehouse, including where logic belongs, and resolve disputes about which version of a number is correct.
- Re-architect a warehouse area that has become slow or expensive, and quantify the improvement before and after.
- 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.
- Own the metric governance framework for the organization and arbitrate the highest-stakes disputes about what a headline number means.

REQUIREMENTS
- Experience at the Lead Data Scientist level (level 4 of 5) or equivalent
- Holds 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 design the pipeline architecture for a new data domain, including how failures are surfaced and how a bad run is safely reversed.
- 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 re-architect a warehouse area that has become slow or expensive, and quantify the improvement before and after.

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 set the documentation and lineage a dataset must carry before others may depend on it, and get teams outside your own to adopt that standard.
- Data Ethics and Privacy — You can set the organization's data ethics and privacy standards, including who may access which data and how automated outputs are explained.
- Analytical Quality Assurance — You can set how the organization scales review to risk and who must sign off business-critical analysis before it is used.

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 set standards for when a predictive or AI model is worth using and how it is evaluated, and test a team's or vendor's claims against evidence.
- Feature Engineering — You can build features from messy or sparse data so that training and production compute the same values, and review others' features for leakage and drift.
- Forecasting and Trend Analysis — You can set how the organization forecasts and states uncertainty in planning numbers, and retire methods that keep missing.
- Data Exploration and Preparation — You can set how exploration and preparation are done and evidenced across the organization, so any finding can be reproduced by someone else.
- 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 set the charting standards and shared component library for the organization, including accessibility requirements and which presentations are not acceptable.
- 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 set how analysis is reported across the organization so reports lead to decisions, and get leaders to change course on evidence.

Delivery
- Project Management — You can keep several people's work in step across teams, agree priorities and hand-offs with them, and keep one shared task list that all of them actually use.
- 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 write up your approach so others can apply it to that kind of problem.
- Domain Expertise — You can influence how the field is practiced beyond this organization, for example through published work, talks or work on shared standards.
- Continuous Learning — You can set what the organization invests its learning time in, and build learning material and practice that teams keep using without you.
- Digital Tools & AI Use — You can decide where AI and automation fit in several teams' work, set the human checks and data rules they follow, and help people adopt the tools safely.

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 write documents that others can keep current after you move 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 and own its riskiest decisions, including the unpopular ones.
- Mentoring — You can shape how the organization grows its people, including how mentors are chosen and supported and how their impact is checked.
- Strategic Thinking — You can shape the organization's strategy, argue for the bets it should make, and review those bets against what happened.

REQUIRED CERTIFICATIONS
- Salesforce Certified Tableau Desktop Foundations (required from Data Analyst)