Data Scientist, District GTM

Class Dojo's goal is to give every child on Earth an education they love.

We started by building a powerful network for communication. Class Dojo’s flagship app is the #1 communication app connecting K-12 teachers, children, and families globally. Teachers use it to share what’s happening throughout the day through photos, videos, and messages that make parents feel like they’re there. It’s actively used in over 95% of US schools, reaching over 45 million children in 180 countries, with a team of just around 200 people [1].

We are now beginning to use this network to give kids the best learning experiences in the world, far beyond those a standard school can provide.

We hire for talent density. Our team comprises the most talented, entrepreneurial, and innovative teammates from around the world, with experience in education and large scale consumer internet companies, including Instagram, Netflix, Dropbox, Stripe, Uber, Y Combinator, and more. We’re building a company where the most talented people want to work. We believe you’ll do the best work of your life here—and you’ll pioneer the future of education, too.

As a Staff Data Scientist embedded with our District GTM team, you'll own the analytical work supporting Class Dojo's district growth. You'll work directly with Sales, Marketing, and Product leadership to build the systems, find the insights, and inform the decisions that move the business forward.

The scope runs from raw CRM and product usage data through to the recommendations that shape how we sell, market, and prioritize. You'll build the infrastructure your work depends on, define what good data looks like, and make sure the analysis actually reaches decisions.

At Class Dojo, Staff means setting the direction. You'll have real influence over how the GTM team operates, with leadership treating you as a peer.

• Build a pipeline analytics system the GTM team can use on their own.

  Today, our district sales org has multiple competing sources of truth (CRM, product usage, and marketing signals) and relies on ad hoc analysis to answer basic pipeline questions. The goal is a single reliable reporting model that integrates all three data sources, standardized GTM metrics, and self-serve dashboards that let Sales check pipeline health without filing a data request.

• Make data a genuine input to GTM decisions.

  You'll identify the key drivers of pipeline conversion (e.g. teacher adoption signals, district size, persona engagement), build models that predict deal likelihood, and run structured experiments with Sales and Marketing that measurably change how the team operates. In 18 months, GTM leadership will reach for data first when making targeting, messaging, and prioritization calls.

• Make core B2B datasets reliable enough to build on.

  CRM, marketing, and product datasets arrive fragmented and inconsistently defined. You'll clean and structure them into stable analytical models with documented definitions, identify instrumentation gaps and resolve them with engineering, and create a foundation where new GTM analytics work streams can be stood up quickly.

• Work alongside District GTM leadership as a trusted analytical partner.

  You'll regularly work with District GTM leadership as they frame key business questions and weigh tradeoffs. You'll translate ambiguous problems into clear analytical approaches, communicate findings directly, and inform long-term decisions about how and where we grow.

• Our stack includes the following technologies:

  Salesforce (CRM)
  · SQL
  · dbt
  · Amazon Redshift
  · Metabase
  · Airflow
  · Git Hub

• Business judgment

  You identify what actually matters in a dataset and why it matters for the business. You ask sharp questions that surface the real problem, connect analytical results to concrete GTM actions, and make sound calls when the data is incomplete.

• Comfort with messy B2B data

  District GTM data arrives from multiple systems with inconsistent definitions and incomplete coverage. You treat that as a normal constraint. You can debug a messy Salesforce dataset, design robust pipelines despite imperfect inputs, and propose structural fixes when the…