Jobs via Dice
Job Title
Senior Snowflake Data Scientist
Location Charlotte, NC (Hybrid)
Contract Type Long-term contract
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
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.
Feature Engineering: Utilize advanced SQL and Python/Snowpark to perform large-scale feature engineering, data transformation, and preparation directly within Snowflake.
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.
MLOps & Production Deployment
Operationalization: Lead the deployment of trained models into production environments, utilizing Snowpark, Snowflake UDFs/UDTFs, and external functions.
Pipeline Automation: Collaborate with Data Engineering to integrate ML pipelines into CI/CD workflows, ensuring models are automatically retrained and redeployed.
Monitoring: Establish and maintain robust monitoring for model performance (drift, bias, accuracy) and operational health.
Data Visualization & Storytelling
Insight Generation: Conduct deep-dive exploratory data analysis (EDA) using complex Snowflake SQL.
Visualization & Communication: Effectively communicate analytical findings, model outputs, and recommendations to stakeholders using Tableau, Power BI, or Snowflake Streamlit.
Platform & Technical Leadership
Best Practices: Define and promote best practices for statistical rigor, ML coding standards, and efficient data processing.
Mentorship: Provide technical guidance and mentorship to junior data scientists and analysts.
Innovation: Stay current with the latest features of the Snowflake Data Cloud and propose innovative ways to leverage them.
Minimum Qualifications
MS or Ph.D. in a quantitative discipline (e.g., Statistics, Computer Science, Engineering, Economics, or Mathematics).
7+ years of progressive experience in Data Science, with at least 3+ years of hands‑on experience building and deploying ML solutions in a cloud data warehouse environment, preferably Snowflake.
Expert proficiency in Python (including scikit‑learn, NumPy, Pandas) and writing scalable data‑processing code.
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 Google Cloud Vertex AI) integrated with Snowflake.
Experience with workflow orchestration tools (e.g., Apache Airflow, dbt).
Seniority Level Not Applicable
Employment Type Full-time
Job Function Engineering and Information Technology
Industries Software Development
#J-18808-Ljbffr
Location Charlotte, NC (Hybrid)
Contract Type Long-term contract
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
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.
Feature Engineering: Utilize advanced SQL and Python/Snowpark to perform large-scale feature engineering, data transformation, and preparation directly within Snowflake.
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.
MLOps & Production Deployment
Operationalization: Lead the deployment of trained models into production environments, utilizing Snowpark, Snowflake UDFs/UDTFs, and external functions.
Pipeline Automation: Collaborate with Data Engineering to integrate ML pipelines into CI/CD workflows, ensuring models are automatically retrained and redeployed.
Monitoring: Establish and maintain robust monitoring for model performance (drift, bias, accuracy) and operational health.
Data Visualization & Storytelling
Insight Generation: Conduct deep-dive exploratory data analysis (EDA) using complex Snowflake SQL.
Visualization & Communication: Effectively communicate analytical findings, model outputs, and recommendations to stakeholders using Tableau, Power BI, or Snowflake Streamlit.
Platform & Technical Leadership
Best Practices: Define and promote best practices for statistical rigor, ML coding standards, and efficient data processing.
Mentorship: Provide technical guidance and mentorship to junior data scientists and analysts.
Innovation: Stay current with the latest features of the Snowflake Data Cloud and propose innovative ways to leverage them.
Minimum Qualifications
MS or Ph.D. in a quantitative discipline (e.g., Statistics, Computer Science, Engineering, Economics, or Mathematics).
7+ years of progressive experience in Data Science, with at least 3+ years of hands‑on experience building and deploying ML solutions in a cloud data warehouse environment, preferably Snowflake.
Expert proficiency in Python (including scikit‑learn, NumPy, Pandas) and writing scalable data‑processing code.
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 Google Cloud Vertex AI) integrated with Snowflake.
Experience with workflow orchestration tools (e.g., Apache Airflow, dbt).
Seniority Level Not Applicable
Employment Type Full-time
Job Function Engineering and Information Technology
Industries Software Development
#J-18808-Ljbffr