Key Responsibilities:

• Team Leadership & Mentorship: Hire, lead, coach, and develop a high-performing team of product and marketing analysts.
• Analytics Strategy & Execution: Own the company's marketing measurement, experimentation, and insight generation for paid, owned, and integrated channels, and for performance across acquisition, activation, engagement, retention, and LTV.
• Hands-On Analysis: Perform deep-dive analyses to uncover insights that inform product and business decisions.
• Cross-Functional Collaboration: Act as a strategic thought partner to identify opportunities, measure success, and optimize Marketing and product performance.

Requirements:

• 10+ years of experience in analytics, with at least 2 years in a people management role.
• Strong technical skills in SQL, Python, data visualization tools (Amplitude, Tableau) and experimentation.
• Has direct experience in marketing analytics using Media Mix Models (MMM), difference-in-differences and other causal inference techniques.
• Proven track record of leading high-performing marketing or product analytics teams.
• Excellent communication and storytelling skills.
• Ability to balance strategic thinking with hands-on execution.

XML job scraping automation by YubHub

As a Staff Data Scientist, you will be a key strategic partner to the Growth (Product and Engineering), Marketing, Sales, and Finance & Strategy teams, developing both intelligent data products and insights, and creating end-to-end systems and measurement plans for accelerating Stripe's overall growth engine.

Responsibilities:

• Provide direction to cross-functional partners on business strategy for enabling Stripe's growth, leveraging your expertise in causal inference / experimentation, modeling, analytical insights, and data foundations.
• Provide senior technical direction to data teams on horizontal technical areas, including experimentation, attribution, forecasting, observability, etc.; assume hands-on leadership, especially when helping teams resolve complex problems through iterative execution.
• Identify broad company problems and opportunities that can be tackled through data science; work with relevant teams to design and build the data science outputs that deliver outsized value to our users and our business.
• Contribute to the overall strategy, roadmap, and vision of your data science team and organization.
• Evangelize and inspire best practices across data science; lead by example to build a culture of craftsmanship and innovation.
• Provide mentorship to our data science talent to help them grow technically and professionally.

Minimum requirements:

• 10+ years of data science experience OR equivalent combined industry and research experience in a quantitative field.
• B.S. / M.S. / Ph.D. in a quantitative field (e.g. Statistics, Mathematics, Economics, Operations Research, Quantitative Marketing, Physical Sciences, Engineering, etc.).
• Experience with modern causal inference techniques.
• Demonstrated experience of leading organization-wide initiatives spanning multiple teams, or leveraging deep domain expertise to influence tech roadmap planning and execution.
• Demonstrated ability to effectively collaborate across multiple teams and stakeholders to drive business outcomes.
• Experience creating alignment with stakeholders in ambiguous and complex situations.
• Demonstrated ability to balance execution and velocity with research, statistical depth, and scalable design.
• Experience, mentoring, and investing in the development of peers.

XML job scraping automation by YubHub

In this role, you'll apply strong analytical judgment and product intuition to help us understand customer behaviour, evaluate trade-offs, and make smart investment decisions across the cards and lending lifecycles , from eligibility and activation to spend, retention, incentives, and credit performance. You'll help build a data-informed culture across Mercury so teams can move quickly, measure what matters, and invest intelligently.

Key responsibilities include:

• Bringing impeccable communication and complete ownership , independently identifying opportunities, developing strong points of view, and influencing executives, Cards & Credit leaders, and cross-functional partners through clear, concise, and persuasive storytelling.
• Developing a nuanced understanding of cardholder behaviour and economics, helping teams reason about trade-offs between growth, engagement, risk, and unit economics.
• Defining, owning, and analysing metrics that inform both tactical decisions and long-term strategy across the cards and credit lifecycle (e.g., eligibility, activation, spend, utilisation, rewards, retention, loss signals).
• Designing and evaluating experiments using rigorous statistical approaches, including A/B testing, cohort analysis, causal inference techniques, and trend analysis.
• Building and improving data pipelines and tools to streamline data collection, processing, and analysis workflows, ensuring the integrity, reliability, and security of data assets.
• Building and deploying predictive models to forecast key outcomes, inform product treatments, and deepen understanding of causal drivers.

Requirements include:

• 7+ years of experience working with large datasets to drive product or business impact in data science or analytics roles.
• Fluency in SQL and comfort with Python.
• Strong judgment in defining and analysing product metrics, running experiments, and translating ambiguous questions into structured analyses.
• Exceptional proactivity and independence , identifying opportunities, forming strong points of view, and making your case to stakeholders.
• Experience with ETL processes and modern data modelling (e.g., dbt, dimensional models, Airflow), with a solid understanding of how data is produced and consumed.
• Experience in analytical approaches ranging from behavioural modelling to experimentation to optimisation , and, importantly, know when simpler approaches are the right answer.
• Apply AI tools to accelerate analytical and business workflows, improving scalability, decision quality, and reducing manual or repetitive work across teams.

Nice to have:

• Experience working on cards or credit products, with familiarity in card economics and lifecycle concepts (e.g., spend behaviour, interchange, rewards and incentives, utilisation, credit limits, retention).
• Experience developing quantitative pricing models or engines (e.g., dynamic pricing, incentive optimisation, or marketplace pricing systems).
• Experience applying optimisation techniques to resource allocation or decision systems (e.g., customer operations, capacity planning, or policy optimisation).
• Experience building or supporting credit models, including probability of default modelling, cashflow modelling, or dynamic credit limit setting.

XML job scraping automation by YubHub