Description

You will apply machine learning and advanced analytical techniques to solve complex business problems through the full data science lifecycle.

Responsibilities

• Drive projects through data collection, exploration, model development, validation, and deployment.
• Seek, build, and consolidate data inputs proactively to improve existing models.
• Translate business requirements into data science projects and make technical decisions based on complexity versus value.
• Develop presentations to communicate key messages and insights to senior sponsors and non-technical stakeholders.
• Work with technical partners to deploy models and streamline processes for scaling.

Required Skills

• 5+ years of experience building and deploying models into production.
• B.S. degree in a quantitative field such as Statistics, Engineering, Computer Science, Mathematics, Economics, or Operations Research.
• Proficiency in Python and/or R for writing clean, efficient, and reusable code.
• Expertise in writing efficient SQL queries from scratch to blend data from various sources.
• Deep understanding of machine learning algorithms, including their implementation, benefits, and drawbacks.
• Experience using statistical methods and tests to investigate trends, patterns, and causal relationships.
• Ability to visualize data-driven stories and monitor model performance.
• Proficiency with SQL, AWS, Snowflake, and Microsoft PowerBI.

Preferred Skills

• Graduate degree (Masters or PhD) in a quantitative field.
• Experience using git for version control and collaboration.
• Expertise with AWS services including SageMaker, S3, and Redshift.

Key Skills

Education

Graduate degree