Data Scientist, G2

• 4+ years experience as a data scientist involved in data extraction, analysis and modeling
• 4+ years of experience in Python and SQL
• Strong understanding of statistics
• Proficiency in machine learning algorithms and all stages of machine learning
• Familiarity with neural networks and deep learning
• Familiarity with AWS services and Snowflake (or similar SQL DB)
• Familiar with containerization (e.g., Docker) and API frameworks (e.g., Flask)
• Demonstrated ability to troubleshoot issues in production environments, including debugging data pipelines or model related errors

Desirable

• As a Data Scientist, you will focus on developing machine learning models to solve complex business problems.
• In addition to core data science responsibilities, you will occasionally step into ML engineering tasks, such as troubleshooting pipelines and ensuring operational stability.
• This role is ideal for a data scientist with strong technical skills and an interest in supporting model deployment and production workflows when needed.
• You'll also mentor junior team members and foster collaboration across teams, contributing to a culture of shared knowledge and continuous improvement
• Lead the development and refinement of machine learning models, including feature engineering, algorithm selection, and model optimization
• Conduct experiments with advanced machine learning techniques to improve model performance and deliver impactful solutions
• Build, maintain, and optimize data pipelines to support end-to-end machine learning workflows
• Analyze large datasets to extract insights and provide actionable recommendations for business teams in conjunction with model development
• Collaborate with ML engineers to operationalize models, ensuring scalability and reliability
• Work closely with cross-functional teams, including product managers and engineers, to translate business requirements into machine learning solutions
• Document and present methodologies, findings, and results to both technical and non-technical audiences
• Act as an on-call resource to troubleshoot and resolve issues with deployed machine learning models
• Collaborate with ML engineers to monitor model performance and ensure operational stability
• Mentor junior team members, providing technical support, guidance on model development, and best practices implementation