Design, develop, deploy, and monitor scalable data science and machine learning solutions in production environments.
Build and maintain robust data pipelines supporting data ingestion, transformation, feature engineering, and model deployment.
Leverage Snowflake for data engineering, analytics, model integration, and operationalization of data products.
Develop predictive models, statistical analyses, and machine learning algorithms using Python and relevant data science libraries.
Write, optimize, and maintain complex SQL queries to support analytics, reporting, and model development.
Work across the full data science lifecycle, including data acquisition, exploration, modeling, validation, deployment, monitoring, and continuous improvement.
Collaborate with business stakeholders, product teams, and engineering teams to translate business requirements into analytical and machine learning solutions.
Communicate technical findings, recommendations, and business impact effectively to both technical and non-technical audiences.
Ensure data quality, model performance, and operational reliability through ongoing monitoring and governance practices.
Required Qualifications
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
Strong hands‑on experience with Snowflake, including solution development, deployment, optimization, and monitoring.
Advanced proficiency in Python, including experience with pandas, scikit-learn, and machine learning pipeline development.
Strong SQL expertise with the ability to write and optimize complex, high‑performance queries.
Demonstrated experience building, deploying, and maintaining production‑grade data science and machine learning solutions.
Experience working across the complete data science lifecycle from data ingestion through deployment and monitoring.
Strong analytical, problem‑solving, and communication skills with the ability to clearly articulate technical solutions and business outcomes.
Preferred Qualifications
Experience with Snowpark (Python and/or Snowpark ML).
Exposure to Streamlit, Dash, or similar frameworks for developing data applications and analytical tools.
Experience working with both structured and semi‑structured datasets.
Background in healthcare, life sciences, financial services, or other regulated industries.
Experience building internal‑facing tools, applications, dashboards, or self‑service analytics solutions used by business stakeholders.
Familiarity with MLOps, model monitoring, CI/CD pipelines, and cloud‑based data platforms.