About the Role
- Are you an experienced Applied Data Scientist with over 3 years of experience, eager to leverage robust technical and analytical skills to propel Google DeepMind’s mission of bringing the benefits of AI to the world? DeepMind is offering a Fixed Term Contract (FTC) role within its Core Analytics Team.
- At Google DeepMind, Artificial Intelligence is regarded as one of humanity’s most potentially useful inventions. The organization comprises scientists, engineers, machine learning experts, and other professionals who collaborate to advance the state of the art in AI. Their technologies are applied for widespread public benefit and scientific discovery, with a strong emphasis on critical challenges and ensuring safety and ethics.
- The Core Analytics Team (CAT) at Google DeepMind is a full-stack data science unit. They organize, model, and deploy data to inform DeepMind’s strategy and decisions, combining their technical expertise with strong stakeholder relationships to deliver significant impact. This role is specifically focused on applying data science techniques—particularly advanced SQL, Python for analysis, and statistical methods—to drive strategic decisions and generate insights, rather than on deep machine learning model development.
- The Applied Data Scientist will be responsible for translating ambiguous business problems into structured, data-driven analyses that directly influence organizational decisions and change. This position will focus on data-driven decision-making within research planning and People & Culture teams, providing critical insights into areas such as the measurement of research impact, investment strategy, attrition, and employee engagement. The successful candidate 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.
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.
Requirements
- Experienced data professional (3+ years of experience as a e.g. Data Scientist, Data Analyst, Quantitative Analyst, Product Data Scientist) with a proven ability to translate complex business or operational challenges into impactful data-driven solutions and strategic recommendations.
- 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.
Qualifications
- 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.
Benefits
Not explicitly mentioned.