Data Scientist

Roles and Responsibilities

• 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.

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