Lead Data Scientist job description

Data and analytics — Lead Data Scientist
Sets modelling and experimentation direction across a team.

REQUIRED SKILLS
Data Engineering
- Analytical SQL Querying — You can diagnose and rewrite a query that is timing out a production dashboard, and you set the SQL style and review standards your team follows.
- Dimensional Data Modelling — You can redesign a model that has drifted from the business it describes, migrating its consumers without breaking their reports.
- 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 rework a transformation layer that has accumulated silent assumptions, replacing them with tested, documented logic.
- Data Warehouse Architecture — You can re-architect a warehouse area that has become slow or expensive, and quantify the improvement before and after.
- Data Pipeline Monitoring and Alerting — You can design monitoring across a chain of dependent pipelines so a failure is traced to its source instead of surfacing in five dashboards downstream. You cut noisy alerts others have set too loosely.

Data Governance
- Data Quality Monitoring — You can set the quality bar for a data domain, decide what blocks a pipeline versus what only warns, and drive a fix upstream rather than patching downstream.
- Metric Definition and Governance — You can set the metric governance process for a domain, including how a new metric gets approved and where its single source of truth lives.
- 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 Privacy and Compliance — You can set the privacy review process for new data uses in a domain, and judge when a proposed use needs legal or compliance sign-off before it proceeds.
- Role-Based Data Access Control — You can design access for data of mixed sensitivity spread across several systems, balancing what analysts need against the exposure it creates. You are who people ask when a request fits no existing role.

Statistics & ML
- Statistical Analysis and Inference — You can design the statistical approach for a complex or contested analysis, and catch a misuse of statistics in someone else's work before it ships.
- Experiment Design and A/B Testing — You can set the experimentation standards for a product area, including guardrail metrics, and stop a launch being pushed through on a cherry-picked result.
- Predictive Modelling Fundamentals — You can take a model from prototype to a production prediction that a team relies on, including monitoring for its performance degrading over time.
- Feature Engineering — You can decide which features a model needs from the data available, test whether they actually improve it, and drop the ones that do not earn their place. You keep them interpretable enough to explain to the business.
- Forecasting and Trend Analysis — You can forecast where the pattern breaks, such as a launch, a promotion or a structural shift, and say plainly what the model cannot know. You review forecasts before they drive a commitment.

Visualisation
- Dashboard Design — You can set the dashboard standards for a domain, including performance and definition consistency, and retire dashboards nobody uses.
- Data Visualisation 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.
- Exploratory Data Analysis — You can explore a large or poorly understood dataset where nobody can tell you what a field means, and reconstruct the truth from the data itself. You catch the flaw others missed before a conclusion is published.
- Stakeholder Analytics Translation — You can manage analytics relationships with senior stakeholders across a function, setting expectations about what data can and cannot tell them.
- 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 run work spanning several teams, negotiate scope and sequencing with their owners, and maintain one shared plan that all of them actually use.
- Planning & Estimation — You can estimate work spanning several teams, name the assumptions each figure rests on, and re-cut the plan as those assumptions break.
- Ownership & Accountability — You can hold accountability for outcomes delivered mostly by other people, absorbing the blame when it fails and passing on the credit when it works.
- Quality Focus — You can design the quality practice for complex work owned by several teams, and you anticipate the failure modes that only appear once systems interact.

Craft
- Problem Solving — You can solve problems in domains where you are not the expert, and your solutions hold up on cost, performance, and maintainability at once.
- Domain Expertise — You can bring in practice from outside the organisation and make it work here, and your judgement demonstrably improves the projects you touch.
- Continuous Learning — You can judge which new ideas are worth the team's time and which are not, and you make room for the people around you to learn too.

Communication
- Communication — You can bring disagreeing groups to a shared understanding, and colleagues come to you for help framing a difficult or sensitive message.
- Collaboration — You can align teams with competing priorities on a common goal, surfacing the conflict early instead of letting it harden into resentment.
- Technical Writing — You can own the documentation of a large project, coordinating contributions so the work can be maintained by people who never built it.
- Stakeholder Management — You can hold senior and external relationships, negotiate between competing demands, and deliver unwelcome news without losing trust.

Leadership
- Leadership — You can lead across team boundaries, build the credibility that makes people follow you by choice, and create room for others to lead.
- Mentoring — You can develop other mentors, coach people through career decisions rather than tasks, and lift the capability of a whole team.
- Strategic Thinking — You can set direction for an area, choose deliberately what not to do, and defend that choice when it is challenged.

REQUIRED CERTIFICATIONS
- Salesforce Certified Tableau Desktop Foundations (required from Data Analyst)
- AWS Certified Data Engineer - Associate (required from Senior Data Analyst)
- Databricks Certified Data Engineer Associate (required from Senior Data Analyst)
- Google Professional Data Engineer (required from Lead Data Scientist)