Data Scientist, Consumption Analytics and Forecasting

Summary

The Data Scientist position at Temporal Technologies will play a key role in extracting meaning out of our data to support analysis and operations. This position sits at the intersection of Finance, Data, and Strategy, with primary responsibility for building and maintaining scalable models that translate product usage and customer behavior into accurate revenue and COGS projections.

You will partner closely with Finance, Commercial, and Data leaders to support forecasting, planning, and investor-facing analyses, with a particular focus on the dynamics of usage-based revenue, expansion, churn, and infrastructure costs. This position requires an experienced data science professional with strong statistical fundamentals and software engineering skills. The work we do is centered around providing value to our internal stakeholders, and ultimately our customers. We stay laser focused on prioritizing work that contributes to tangible, positive business outcomes.

The ideal candidate is a fast learner with a deep curiosity about how Temporal operates and where data can unlock value. As a Data Scientist at Temporal, you will apply rigorous statistical methods and a wide range of machine-learning techniques to identify opportunities that help our customers achieve outstanding outcomes. You will partner closely with stakeholders across multiple teams, uncovering meaningful signals that inform strategy and drive measurable impact. This role offers the opportunity to design and build data products and analytical solutions from the ground up using best-in-class technologies—while collaborating with exceptionally talented and supportive teammates.

What You’ll Do

• Build and maintain forecasting models for consumption-based revenue and related cost of goods sold (i.e. cloud costs).
• Partner closely with Finance and other stakeholders to understand business mechanics, modeling assumptions, and planning objectives, and translate these into robust analytical frameworks.
• Analyze complex customer and usage data, including sparse, early-stage, and rapidly scaling cohorts - to identify durable signals around retention, expansion, and monetization.
• Deliver high-quality analytical outputs, including ML models, exploratory analyses, dashboards, visualizations, and data-driven recommendations.
• Monitor and evaluate model performance over time, detecting drift and developing challenger models to ensure accuracy and resilience.
• Conduct causal and attribution analyses to understand the drivers of revenue growth, expansion, and cost efficiency.
• Communicate insights effectively to technical and non-technical audiences through clear visualizations, narratives, and presentations - including support for executive and investor discussions.

What You'll Bring

• Strong foundation in statistics, experimental design, and quantitative reasoning.
• 3+ Experience working with revenue, pricing, usage-based, or subscription data, ideally in a SaaS or infrastructure context.
• Hands-on experience with a range of supervised and unsupervised ML techniques.
• A results-oriented mindset with a focus on driving measurable business outcomes.
• Ability to prototype quickly in notebooks (Jupyter, Marimo, Colab) and deploy models in production environments.
• Experience with at least one major cloud provider (AWS, GCP, or Azure).
• Proficiency in Python and SQL; familiarity with additional languages is a plus.
• Experience working with modern data processing and query engines (e.g. Presto/Trino, Athena, BigQuery).
• Familiarity with both object-store and relational database technologies (e.g., S3, Redshift, Postgres).
• Comfort operating in environments with imperfect data definitions, evolving metrics, and trade-offs between precision and decision usefulness.
• Demonstrated ability to learn new tools, frameworks, and technologies quickly.
• Curiosity and enthusiasm for experimentation, iteration, and continuous learning.
• Clear communicator who can explain complex modeling decisions and insights in plain language, tailoring the level of technical detail to the audience.