GTM Data Analyst - Target Modelling

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.

We are looking for a Senior Data Scientist to join our Global GTM Target Setting Competency Hub. This central analytics team plays a pivotal role in supporting Stripe’s Go-To-Market (GTM) and Marketing organizations. Our team collaborates with Finance & Strategy (F&S), Global Planning, Marketing Operations and our wider GTM Revenue Data Science team to align the Go-To-Market teams towards common goals, fostering a culture of accountability and empowering informed decision-making. 

As a Senior Data Scientist, you will be at the forefront of shaping Stripe's commercial ambitions. You won't just be analyzing data; you'll be architecting the future of our target-setting frameworks, directly influencing resource allocation, strategic planning, and ultimately, Stripe's success. In today’s rapidly evolving market, maintaining target setting integrity and fostering innovation are crucial for sustained success.  

• Goal Setting: Collaborate closely with leadership and cross-functional teams to establish data-driven goals and performance metrics that align with overall business objectives. Your expertise in target setting will be crucial to translating strategic goals into measurable and achievable targets at all levels of the organization.
• Forecasting & Modeling: Design and implement sophisticated predictive models and tools for sales forecasting and creating dynamic target allocation systems ensuring the connectivity of metrics throughout the sales funnel. Proactively identify opportunities for innovation in target setting, leveraging cutting-edge data science techniques and industry best practices. Lead the design and execution of rigorous experiments to evaluate the effectiveness of different target-setting methodologies and drive continuous improvement.
• Communication of complexity: Develop compelling data visualizations and narratives to communicate complex findings and recommendations to both technical and non-technical audiences, including executive leadership. Our predictive models need to be understandable and credible to stakeholders, including business leaders, sales teams, and marketing teams.  Oversee implementation of enabling systems and tools, working closely with internal systems teams communicate goals and progress towards goals clearly for the wider organisation. 
• Process, Policy and Tooling: Contribute to foundational principles and policies on how we measure success of teams and individuals.  Define and prioritize the development of tools and platforms that streamline the target-setting and target cascade process,  and  facilitate more data-driven decision-making in the process. 
• Metric design: Utilize statistical techniques to assess scenarios and quantify the potential impact of business outcomes on metric design and attainment distributions. 
• Growth Analytics Innovation: Lead or contribute to exploratory initiatives aimed at uncovering new and innovative ways to leverage data science for driving Stripe's growth including in the optimisation of territory design. Engage in research and experimentation with cutting-edge analytical techniques, exploring new data sources, and developing novel approaches to target setting and performance management.  Stay abreast of the latest advancements in data science, machine learning, and the SaaS industry to ensure Stripe remains at the forefront of analytical capabilities.
• Mentorship: Act as a mentor to junior data scientists and analysts, sharing best practices in data analysis, goal management, and modeling techniques to enhance team capabilities and foster professional growth.

Required Skills and Qualifications

• 8+ years of experience in data science partnering with business functions such as Revenue Operations, Finance, Growth, Marketing, preferably within the tech or financial services industry. You have a proven track record of independently leading go-to-market data science projects from problem definition to impactful business outcomes.
• You are comfortable with ambiguity and motivated by business results. This role would require a strong intellectual curiosity and a passion for pushing the boundaries of what's possible with data.  

Technical Skills

• You have expertise in SQL for data extraction and manipulation.
• Skills in at least one scripting language (Python and/or R) for statistical analysis and modeling preferable.
• Experience with data visualization tools such as Tableau, Power BI, or similar.
• Familiarity with statistical modeling techniques, machine learning, and A/B testing.
• You are comfortable transforming raw data to build your own data sets if they don't exist yet
• Analytical Skills: Strong analytical and problem-solving capabilities with an ability to draw actionable insights from complex datasets.
• Business Acumen: Understanding of sales and marketing processes, including experience with KPI development and performance measurement.
• Communication Skills: Excellent verbal and written communication skills, with the ability to present complex data analysis to non-technical stakeholders clearly and effectively.
• Collaboration: Demonstrated ability to work effectively in a cross-functional team environment and influence decision-making through data-driven insights.
• You have a bias for using the right tools to get a job done with maximum efficiency. You have experience making tradeoffs between speed and accuracy

• Experience with the Salesforce - knowledge of Salesforce data management, system configuration mechanics, and experience building Salesforce reports and dashboards.
• Experience with Anaplan for Headcount Management, Territories, Quota, or Productivity