Manager, Analytics
JobberSalary not specified
Not SpecifiedCompensation
Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
Artificial Intelligence, TechnologyIndustries
Requirements
- Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field
- 5 years of experience with analysis applications (e.g., extracting insights, performing statistical analysis, or solving business problems), and coding (e.g., Python, R, SQL) or 2 years of experience with a Master's degree
- 2 years of work experience identifying opportunities for business/product improvement and then defining/measuring the success of those initiatives
- Proven experience in identifying data-driven opportunities for business/product improvement and defining/measuring the success of those initiatives (advantage)
- Proven experience in setting up, maintaining, and reporting on top-line product performance and business metrics (advantage)
- Strong communication, writing, and presentation skills (advantage)
- Past experience on a performance or growth data science or similar team (advantage)
- Experience with experimental design and analysis (advantage)
- Experience working with large and messy datasets to solve ambiguous business problems (advantage)
- Ability to self-start and self-direct work in an unstructured, fast-paced, challenging environment (advantage)
- A bias for action and creative problem-solving and ability to work effectively across functions and PAs (advantage)
Responsibilities
- Translate ambiguous questions into well-defined problems
- Analyze large complex datasets to produce concise, actionable insights
- Communicate findings and recommendations to executive stakeholders, including visualizing data in a clear, compelling way
- Develop, implement, and track top-level product and business metrics
- Dive into metric developments and changes, and identify key drivers and root causes
- Automate currently manual metric reporting flows and outputs
- Build and deploy statistical/ML models to understand our users and product capabilities
- Partner with product, engineering, and UX to develop data-driven product insights and strategies
- Champion data-driven culture by feeding user engagement insights back into models
Skills
Data Analysis
SQL
Python
Data Visualization
Metrics Development
Statistical Analysis
Executive Communication
Trend Analysis
Root Cause Analysis
Automation
DeepMind
Develops artificial general intelligence systems
About DeepMind
This company leads in the field of artificial general intelligence (AGI), with notable applications across healthcare, energy management, and biotechnology. Their work in early diagnostic tools for eye diseases, optimizing energy usage in major data centers, and groundbreaking contributions to protein structure prediction underlines their commitment to harnessing AI for diverse practical applications. The company's dedication to pushing the boundaries of AI technology not only propels the industry forward but also creates a dynamic and impactful working environment for its employees.
London, United KingdomHeadquarters
2010Year Founded
$4.9MTotal Funding
ACQUISITIONCompany Stage
AI & Machine Learning, BiotechnologyIndustries
1,001-5,000Employees
Benefits
Performance Bonus
Risks
Emerging AI models may challenge DeepMind's current strategies.
Backlash against AI models like Gemini poses reputational risks.
Labeling AI-generated content could increase operational complexity for DeepMind.
Differentiation
DeepMind combines AI, ML, and neuroscience for general-purpose learning algorithms.
DeepMind's AlphaFold model advances protein folding research significantly.
GraphCast by DeepMind offers rapid, accurate ten-day weather forecasts.
Upsides
AI-driven drug discovery is set to grow significantly in 2024.
AlphaCode 2 showcases AI's potential in competitive programming.
DeepMind's AI tools are transforming music creation and meteorology.
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