Senior Snowflake Data Scientist

Job Summary The Senior Snowflake Data Scientist will lead the development, deployment, and operationalization of machine learning and statistical models that solve complex business problems and drive strategic decision-making. This role requires an expert blend of statistical rigor, advanced programming, and deep knowledge of leveraging Snowflake's ecosystem (e.g., Snowpark, Streamlit, external functions) for high-performance, in-warehouse data science. Key Responsibilities 1. Advanced Modeling & Analysis Model Development: Design, build, train, and validate sophisticated machine learning (ML) and statistical models (e.g., predictive, prescriptive, clustering, forecasting) to address key business challenges (e.g., customer churn, sales forecasting, risk modeling). Feature Engineering: Utilize advanced SQL and Python/Snowpark to perform large-scale feature engineering, data transformation, and preparation directly within Snowflake, ensuring high data quality and low latency for modeling. A/B Testing & Causal Inference: Design and analyze experiments (A/B tests) and employ causal inference techniques to measure the business impact of product features, strategies, and model outputs. X. MLOps & Production Deployment Operationalization: Lead the process of deploying trained models into production environments, utilizing Snowpark, Snowflake UDFs/UDTFs, and external functions for scalable inference and real-time scoring. Pipeline Automation: Collaborate with Data Engineering to integrate ML pipelines into CI/CD workflows, ensuring models are automatically retrained and redeployed using tools like Airflow or orchestration platforms. Monitoring: Establish and maintain robust monitoring for model performance (drift, bias, accuracy) and operational health within the Snowflake environment. 3. Data Visualization & Storytelling Insight Generation: Conduct deep-dive exploratory data analysis (EDA) using complex Snowflake SQL to uncover hidden patterns, opportunities, and risks. Visualization & Communication: Effectively communicate complex analytical findings, model outputs, and recommendations to technical and non-technical stakeholders and senior leadership using compelling data storytelling and visualization tools (e.g., Tableau, Power BI, or Snowflake Streamlit). X. Platform & Technical Leadership Best Practices: Define and promote best practices for statistical rigor, ML coding standards, and efficient data processing within the Snowflake ecosystem. Mentorship: Provide technical guidance and mentorship to junior data scientists and analysts on modeling techniques and leveraging Snowflake's data science features. Innovation: Stay current with the latest features of the Snowflake Data Cloud (e.g., Generative AI/LLMs, Unistore, Data Sharing) and propose innovative ways to leverage them for business value. Minimum Qualifications MS or Ph.D. in a quantitative discipline (e.g., Statistics, Computer Science, Engineering, Economics, or Mathematics). X+ years of progressive experience in Data Science, with at least X+ years of hands-on experience building and deploying ML solutions in a cloud data warehouse environment, preferably Snowflake. Expert proficiency in Python (including packages like scikit-learn, NumPy, Pandas) and writing scalable code for data processing. Expert-level command of Advanced SQL for complex data manipulation and feature engineering. Proven experience with Machine Learning algorithms and statistical modeling techniques. Strong understanding of MLOps principles for model lifecycle management. Preferred Skills & Certifications Snowflake SnowPro Advanced: Data Scientist Certification. Hands-on experience developing solutions using Snowpark (Python/Scala). Experience building data apps/dashboards using Snowflake Streamlit. Familiarity with cloud platforms and services (AWS Sagemaker, Azure ML, or GCP Vertex AI) integrated with Snowflake. Experience with workflow orchestration tools (e.g., Apache Airflow, dbt).

Created: 2026-03-04