SR Data Scientist

Key Responsibilities

• Contribute to the strategic direction of the R&D program by identifying emerging data science methodologies and opportunities for application
• Explore generative AI solutions to advance research capabilities and accelerate innovation
• Collaborate with cross-functional R&D teams to understand research objectives and translate them into data-driven analytical solutions
• Design, develop, and deploy machine learning models to solve complex research and analytical challenges within the R&D program
• Create advanced, interactive visualizations and dashboards to communicate research findings to diverse audiences, including leadership and stakeholders
• Conduct rigorous statistical analyses to validate research findings and ensure data integrity across projects
• Lead stakeholder engagement, presenting complex technical and research concepts in accessible ways to both technical and non-technical audiences
• Mentor team members to contribute to the growth of the data science practice within the R&D organization

Required Qualifications

• Python Proficiency: Expert-level skills in Python for data analysis, modeling, and automation (required)
• Data Manipulation & Processing: Strong proficiency in pandas for data analysis and PySpark for large-scale distributed data processing
• SQL: Advanced SQL skills for data extraction, transformation, and analysis across various database systems
• Statistics: Strong foundation in statistical methods, experimental design, and hypothesis testing (required)
• Machine Learning: Hands-on experience building, training, and deploying ML models in production environments
• Advanced Visualization: Proficiency with Plotly and Plotly Dash for creating interactive, production-grade visualizations
• Communication Skills: Exceptional ability to communicate technical findings to both technical and non-technical audiences
• Stakeholder Management: Proven track record of managing stakeholder relationships and driving alignment
• Education: Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or related quantitative field; advanced degree (Master's or PhD) preferred

Additional Qualifications

• Generative AI: Experience with large language models (LLMs), prompt engineering, and GenAI applications
• Experience with graph databases and graph analytics
• Knowledge of network analysis and graph-based algorithms