Senior Applied Data Scientist

Extensive Analytical Experience: Proven track record (typically 5+ years) in data science or analytics roles, demonstrating increasing responsibility and impact.
Strategic Problem Solving & Framing: Ability to independently identify, structure, and lead the analysis of complex, ambiguous strategic questions.
Expert SQL for Complex Analysis: Mastery of SQL for sophisticated data extraction, manipulation, and analysis across large, intricate datasets.
Advanced Python for Data Science: High proficiency in Python and its data science ecosystem (Pandas, NumPy, SciPy, Scikit-learn, visualization libraries) for complex modeling and analysis.
Executive Communication & Influence: Exceptional ability to communicate complex quantitative findings and strategic recommendations clearly, concisely, and persuasively to senior executive audiences. Proven ability to influence decision-making.
Strong Stakeholder Management: Demonstrated ability to build trust and collaborate effectively with senior stakeholders across different functions.
Applied Statistics & Modeling: Deep understanding and practical application of relevant statistical methods and modeling techniques (e.g., regression, classification, experimentation, forecasting).
Mentorship Aptitude: Demonstrated ability or strong interest in coaching and mentoring junior team members.

Strategic Partnership: Work directly with stakeholders, including senior leaders, to identify the most pressing challenges and use data to solve them.
Analysis: Conduct rigorous, end-to-end analyses using SQL, Python, and statistical methods to uncover root causes, model trends, and answer complex questions around our organisational health and effectiveness.
Data Storytelling & Communication: Translate these findings into clear, compelling narratives and drive organisational decisions and change by interacting with diverse audiences (technical and non-technical) via presentations, reports, and dashboards.
Proactive Opportunity Identification: Go beyond assigned tasks to identify emerging trends, potential risks, and new opportunities for analytical intervention that can drive strategic value.
Cross-functional Collaboration: Work effectively with diverse teams (engineering, product, research, operations, HR) to access data, understand context, and ensure recommendations are applicable.
Enablement & Monitoring: Develop and maintain tools (dashboards, reports) to provide ongoing visibility into key metrics and empower stakeholders with self-service analytics where appropriate.
Mentorship & Team Development: Mentor & develop other data scientists, fostering their growth and maximising their impact. Contribute to defining team standards, improving analytical methodologies, and sharing knowledge across the team.

Broad Domain Experience: Experience applying data science across diverse business areas such as R&D, HR/People Analytics, Product Strategy, Finance, or Operations.
Experience with Large-Scale Data Systems: Familiarity with data warehousing concepts and working with large-scale data in cloud environments (e.g., Google Cloud Platform).
Project Leadership: Experience formally or informally leading analytical projects or workstreams, with demonstrated project management capabilities.
Previous Line Management Experience: Experience managing direct reports is a plus.