Data Analyst

Position: Staff Data Analyst, Growth
As a Staff Data Analyst, Growth, you will lead rigorous statistical analyses and experiment design to uncover causal relationships, optimize strategies, and enable data-driven decision-making  will play a key role in ensuring our experiment infrastructure, analytical pipelines, dashboards, and methodologies are robust, automated, and scalable, driving innovation in how we leverage data to power product and user insights.

This role requires someone with strong statistical and technical skills, who is comfortable working across multiple functional areas in a fast-paced, challenging environment, and explaining complex quantitative concepts to a variety of audiences. Your ideal mix of curiosity, technical expertise, and collaborative spirit will allow you to become a trusted partner and the go-to expert for interpreting complex experiments, identifying key drivers of growth, and championing best practices in data analytics.

We are looking to speak to candidates who are based in New York City for our hybrid working model.

Responsibilities

* Surface insights to drive executive decision making across the business

* Apply advanced statistical techniques to identify causal relationships and drive actionable insights for user acquisition, activation, and retention initiatives

* Design and analyze experiments beyond basic A/B testing, leveraging methodologies that improve accuracy and scalability across growth initiatives

* Automate analytical pipelines, develop reporting tools, and maintain certified data codebases to ensure reproducibility, scalability, and efficiency of insights delivery

* Continuously learn about the product, the user ecosystem, and industry best practices to drive innovation and high-quality analysis

* Make the team more efficient:
Research and implement new tooling/processes; seek out and automate existing costly manual processes

* Serve as an analytical and technical thought leader to stakeholders across the analytics, marketing, and growth teams

Skills & Attributes

* 7+ years of experience in analytics roles, with at least 5 years working closely with growth or product teams

* Advanced degree (Master's or Ph.D.) in Statistics, Data Science, or a related quantitative field strongly preferred

* Proficient in statistical methods for causal inference and applied analytics, with experience designing and interpreting complex experiments and addressing distributional challenges such as skewness and kurtosis

* Strong skills in Python, R, and SQL, with a proven ability to automate workflows and build scalable solutions

* Experience converting analytical workflows and methodologies into reusable tools or packages to increase team adoption and efficiency

* Excellent communication skills, capable of explaining complex quantitative concepts to diverse audiences, including non-technical stakeholders

* Demonstrated ability to effectively communicate and set priorities with a variety of key stakeholders at all levels of the organization

* Self-motivated, curious, and collaborative, with a commitment to fostering a positive work environment and contributing to the team's success

About MongoDB

Mongo

DB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. Mongo

DB's unified database platform-the most widely available, globally distributed database on the market-helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, Mongo

DB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and nearly 60,000 customers-including 75% of the Fortune 100 and AI-native startups-relying on Mongo

DB for their most important applications, we're powering the next era of software.

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DB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It's what makes us Mongo

DB.

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