Applied Data Scientist - London, UK - FTC

We are looking for an experienced Applied Data Scientist who is skilled at and motivated by translating ambiguous business problems into structured, data-driven analyses that drive organisational decisions and change.

This role will focus on driving data-driven decision-making in our research planning, and People & Culture teams. You will provide critical insights into areas such as measurement of research impact, investment strategy, attrition and employee engagement.

You will be embedded within the problem domain, working closely with program managers, engineers, the People & Culture team, and leadership to understand their challenges, formulate key questions, and deliver timely insights.

Key responsibilities

• Strategic Partnership: Work directly with stakeholders, including senior leaders, to identify, scope, and prioritise high-impact analytical questions.
• Analysis: Conduct rigorous, end-to-end analyses using SQL, Python, and statistical methods to uncover insights, model trends, and answer complex questions about efficiency, usage patterns, and strategic investments.
• Data Storytelling & Communication: Translate complex analytical findings into clear, compelling narratives and actionable recommendations for diverse audiences (technical and non-technical) through presentations, reports, and dashboards.
• Enablement & Monitoring: Develop and maintain tools (dashboards, reports) to provide ongoing visibility into key metrics and empower stakeholders with self-service analytics where appropriate.
• Identify Data Needs: Collaborate with engineering and product teams to highlight data gaps and advocate for the collection of telemetry needed to improve future analyses and decision-making.
• Team Contribution: Share knowledge, contribute to the team's analytical road map, and help improve our overall processes and best practices.

What We Can Offer You:

• Direct Strategic Impact: Your analysis and recommendations will directly inform critical investment and strategic decisions, influencing our ability to achieve our mission.
• Leadership Exposure: Work closely with senior leaders and key decision-makers, honing your communication and influencing skills.
• Collaborative Environment: Be part of a supportive and highly skilled data & analytics group, learning from peers and contributing to a culture of analytical excellence.

• Analytical Problem Solving: Proven ability to understand ambiguous problems, formulate key questions, and design/execute appropriate analytical approaches.
• Advanced SQL for Analysis: High proficiency in using SQL to extract, manipulate, aggregate, and analyze complex datasets from various sources to answer business questions
• Stakeholder Management & Communication: Strong track record of building relationships, collaborating effectively, and presenting complex findings and recommendations clearly and persuasively to diverse audiences, including senior leadership. Experience in "data storytelling."
• Applied Statistics/Quantitative Skills: Solid understanding and practical application of statistical concepts for analysis (e.g., hypothesis testing, regression, forecasting).
• Delivery & Execution: Ability to manage multiple analytical projects simultaneously, prioritize effectively, and deliver high-quality insights in a dynamic environment. You are comfortable working independently and taking ownership.

• Domain Interest/Experience: Experience with or a strong interest in research (bibliometrics, innovation pathways/lifecycles, and learning more about key areas/topics in AI research) or People & Culture (HR, recruiting, performance, or employee engagement).
• AI Fluency: Ability and curiosity to use AI tools practically and effectively in your work, with a recognition and awareness of AI’s responsible use, risks, and limitations. 
• Python for Data Analysis: Proficiency in Python and common data analysis libraries (e.g., Pandas, NumPy, SciPy, Scikit-learn, Matplotlib/Seaborn).
• Data Visualization/Dashboarding: Experience creating effective dashboards and visualizations using tools like Tableau, Looker, Google Data Studio, or similar.
• Analytics Engineering: Experience designing and implementing ELT workflows (using tools like dagster, dbt)
• Coaching/Mentoring: Experience mentoring others in analytical techniques or tools.