Principal Data Scientist

About the role:

We're looking for a Principal Data Scientist to own Readdle's company-wide data science roadmap and drive measurable business impact across the organization.

This is a role for a hands-on technical leader with a business mindset — someone who sets methodological standards, prototypes and productionizes predictive and prescriptive systems, and serves as the internal authority on machine learning, autonomous systems, experimentation, causal inference, optimization, and advanced analytics.

You'll work closely with Product, Engineering, Marketing, Data Platform, and Analytics to embed data-driven decision-making into how we build and grow.

What you will do:

• Own the Data Science roadmap and its technical architecture across the company, ensuring focus on the highest-leverage opportunities and scalable foundations.
• Hands-on build and productionize DS models end-to-end: from hypothesis → prototype → reliable production systems that optimize the proper business metrics.
• Methodological excellence: create, maintain, ensure adoption, and evolve advanced analytics frameworks and enablement cross-functionally, including:
• experimentation design and analysis
• causal inference and incrementality testing (incl. diff-in-diff, synthetic control, uplift, geo experiments, incrementality, etc.)
• measurement frameworks and proxy metric design
• optimization & prescriptive analytics
• Inference in marketing measurement (incl. MMM and Bayesian measurement where applicable)
• Develop predictive and prescriptive capabilities, including revenue & churn prediction, time-series forecasting, anomaly detection, and decision-optimization systems.
• Define and defend proxy measurements when direct measurement is infeasible; select the right method for the task and clearly articulate assumptions/tradeoffs.
• Partner with Data Platform & Analytics teams to jointly ensure high-quality, well-modeled, accessible SSOT data for data science;
• Act as the technical bridge between Product, Engineering, and Marketing to ensure data science is embedded in product lifecycle decisions (discovery, rollout, iteration).
• Own & drive standards around DS reproducibility, testing, CI/CD, version control, packaging, documentation, and monitoring.
• Mentor Senior Data Scientists; create learning paths, review work for methodological rigor, and build a culture of technical excellence.

About you:

• Proven experience as a Principal / Staff Data Scientist (or equivalent) delivering cross-functional, company-level impact and setting technical direction.
• Deep expertise in advanced analytics and measurement, including strong proficiency in:
• experimentation and causal inference
• incrementality measurement, optimization / prescriptive analytics
• defining proxy metrics and causal graphs
• Strong track record of building and productionizing ML systems (predictive and/or prescriptive), including:
• time series forecasting and anomaly detection
• causal graphs / structural thinking (when appropriate)
• MMM and/or marketing measurement approaches (as applicable to the business)
• Deep Python expertise plus at least one library in each category:
• ML/DL: (e.g., scikit-learn, XGBoost/LightGBM, PyTorch, TensorFlow)
• Statistics: (e.g., statsmodels, SciPy, etc.)
• Causal inference: (e.g., DoWhy, EconML, CausalML, causalimpact, PyWhy)
• Strong engineering and MLOps fundamentals, including experience with:
• Python in production, containers, and distributed computing
• CI/CD, testing, version control, reproducibility best practices
• modern data modeling frameworks (e.g., dbt/dataform), orchestration patterns, and model monitoring
• AI developer productivity tools and strong craftsmanship
• Excellent business acumen, stakeholder management, and communication: ability to translate complex methods into clear decisions, assumptions, and tradeoffs for senior leaders.
• Ability to operate across ambiguity, prioritize ruthlessly, and deliver outcomes with high standards of rigor.