Senior Product Analyst / Data Scientist

Accountabilities:

Define, develop, and maintain core product and business metrics, establishing a reliable single source of truth that supports decision-making across Product, Finance, and Sales.
Build a comprehensive understanding of self-service customers by analyzing usage patterns, customer health, satisfaction, retention risks, unmet needs, and potential growth opportunities.
Collect, process, and analyze large-scale customer, product, and operational datasets to uncover trends, identify business opportunities, and support strategic decisions.
Translate customer demand, business objectives, and commercial plans into accurate capacity forecasts, helping teams make informed capacity allocation decisions.
Monitor and evaluate effective capacity utilization, identifying opportunities to optimize available resources and maximize their commercial value.
Design, assess, and improve pricing and packaging strategies, including guaranteed capacity, on-demand pricing, customer commitments, clawback mechanisms, and provisioned throughput units.
Develop data-driven recommendations for self-service and managed service offerings, covering customer segmentation, pricing structures, capacity planning, and monetization opportunities.
Take end-to-end ownership of analytical projects, from defining business questions and building data pipelines to conducting statistical analyses and delivering production-ready solutions.
Use Python, SQL, statistical methods, and distributed data-processing tools to generate reliable insights and support scalable analytical workflows.
Leverage AI-assisted coding tools to accelerate development and analysis while ensuring the accuracy, quality, and reliability of the resulting solutions.
Translate complex analytical findings into clear, actionable recommendations that influence product strategy, commercial decisions, and operational improvements.
Collaborate with cross-functional stakeholders across Product, Engineering, Applied AI, Finance, Sales, and Operations to turn insights into measurable business impact.
Requirements:
At least 5 years of professional experience in product analytics, data science, business analytics, or a closely related analytical discipline.
Strong technical proficiency in Python and SQL, combined with a solid understanding of statistics and the analytical methods used to interpret complex datasets.
Hands-on experience analyzing large-scale customer, product, and usage data using statistical techniques, analytical frameworks, or distributed data-processing technologies.
Proven ability to take analytical projects from initial business questions through data preparation, analysis, solution development, and production-ready implementation.
Experience defining business and product metrics, building reliable analytical datasets, and establishing consistent reporting frameworks.
Strong commercial and product judgment, with the ability to balance customer value, monetization, business growth, and profitability.
Practical experience with pricing, packaging, monetization strategies, or commercial analytics, ideally within technical, subscription-based, or consumption-based products.
Demonstrated ability to transform complex datasets and analytical findings into clear recommendations that support strategic and operational decision-making.
Familiarity with AI-assisted coding as a core part of the development workflow, including the ability to guide AI tools, validate their outputs, and take full ownership of the final results.
Excellent cross-functional collaboration skills, with the ability to work effectively alongside Product, Engineering, Data, Finance, Sales, and Operations teams.
A proactive, hands-on, and autonomous working style, with the ability to navigate ambiguity and solve problems in technically complex environments.
Strong communication and presentation skills, with the ability to explain technical concepts and analytical insights to both technical and non-technical stakeholders.
Familiarity with large language model (LLM) inference economics is a plus, including concepts such as tokens, throughput, latency, batching, and GPU infrastructure costs.
Genuine curiosity about AI, cloud infrastructure, and emerging technologies, combined with a willingness to continuously learn and take ownership of impactful challenges.
Benefits:
Competitive compensation: Attractive remuneration package aligned with experience and expertise.
Career development: Opportunities for professional growth, continuous learning, and skills development.
Flexibility and ownership: Flexible working arrangements and significant autonomy in managing projects and responsibilities.
Innovative environment: Opportunity to work on complex, high-impact projects at the forefront of AI and cloud infrastructure.
Collaborative culture: Work alongside talented professionals across engineering, data science, product, and commercial teams.
International exposure: Join a diverse, globally distributed environment with opportunities to collaborate across multiple regions.
Meaningful impact: Contribute directly to the development and optimization of AI infrastructure and data-driven business strategies.
Professional autonomy: Take ownership of end-to-end analytical initiatives and influence important product and commercial decisions.
Continuous innovation: Work in a fast-moving environment that encourages bold thinking, experimentation, and constant improvement.
Long-term growth: Opportunity to help shape the future of AI infrastructure while developing expertise in a rapidly evolving industry.