About Katalyze AIKatalyze AI is a fast-growing AI-driven biotech platform company on a mission to make life-saving drugs accessible and affordable for everyone. Our AI Agents help pharmaceutical and biotech companies increase production efficiency, reduce costs, and minimize waste. We're a team of humble, quick-moving, and curious craftspeople working at the intersection of science and AI.About the RoleWe're looking for a Principal Data Scientist to join Katalyze AI and work at the intersection of applied statistics, machine learning, and biotechnology. You'll independently analyze complex scientific and process data build interpretable predictive models, and translate findings into actionable recommendations for enterprise customers in biopharma and advanced manufacturing.This is a high-ownership, customer-facing role. You'll work directly with scientists and engineers at our accounts, not just hand off reports internally.What You'll DoBuild predictive and diagnostic models on scientific and industrial data (time series, multivariate sensor data, spectral data, batch records)Select and apply the right modelling technique for each problem — gradient-boosted trees, Gaussian processes, neural networks, classical statistical models — with transparent reasoning for your choicesApply signal processing and time series methods (Fourier transforms, wavelet analysis, autocorrelation, decomposition, forecasting) to real-world sensor and process dataDesign rigorous model evaluation frameworks: cross-validation strategies for time-series data, SHAP-based interpretability, uncertainty quantification, and statistical significance testingBuild interpretable ML pipelines that surface drivers of variability in ways that satisfy audit and documentation requirementsDesign analytics dashboards that communicate complex statistical findings to manufacturing scientists, quality teams,



and supply chain managersWork closely with the Deployment Strategist to configure and deliver data science components for customer deploymentsPartner directly with enterprise customers to understand their data challenges, deviation patterns, and quality systemsApply LLM-based approaches where appropriate to automate insight generation and multi-step analytical workflowsWhat We're Looking For6+ years of applied data science experience with a strong foundation in statistics and machine learningDeep understanding of how model families work — linear/logistic regression, tree-based models (XGBoost, LightGBM, CatBoost), SVMs, neural networks, transformers — and when to use eachStrong time series expertise: Fourier analysis, wavelet transforms, autocorrelation, stationarity, decomposition, and forecasting (ARIMA, Prophet, and deep learning approaches)Rigorous model evaluation skills: proper train/test design for time-series data, overfitting detection, SHAP and interpretability methods, uncertainty quantificationExperience with Gaussian processes, Bayesian methods, or uncertainty quantificationStrong Python skills: scikit-learn, pandas, numpy, statsmodels, PyTorch or TensorFlowExperience with enterprise data infrastructure — SQL, data warehouses, cloud platforms (Snowflake, Databricks, Redshift)Strong communication skills — able to explain statistical findings clearly to scientists, engineers, and business stakeholdersExperience with scientific or industrial data (sensor streams, spectral data, batch records, LIMS/MES outputs) is a strong plusPhD or Master's in Data Science, Statistics, Chemical Engineering, or related field preferredExperience with LLMs or agentic systems is a plus, not a core requirementML & Analytics: Python, scikit-learn, XGBoost, LightGBM, PyTorch, statsmodelsLLM / Agents: Claude/GPT APIs, LangChain (where applicable)#J-18808-Ljbffr

📌 Principal Data Scientist (Founding) (Toronto)
🏢 Katalyze AI
📍 Toronto