• 5+ years applied experience in data science
• Experience in analytics working with product and user behavior data, e.g., retention or churn analysis
• The ability to translate business requirements into data science solutions
• Proficiency in database modeling, SQL, and data warehousing principles
• Proficiency in developing analysis in Python and experience with relevant ML libraries and frameworks (e.g., pandas, PyTorch, scikit-learn)
• Proficiency with building ML/AI pipelines and relevant tools (e.g., Kedro, MLFLow)
• Strong team player mindset, while able to work under your own initiative and prioritize time and tasks effectively
• Excellent written and verbal communication skills

Desirable

• We are currently looking for a new Senior Data Scientist, to join our Data Insights team and work closely with Clio’s products and business teams
• You will be working alongside a cross-functional team of data scientists, embedded within Clio’s products and business teams, developing AI and ML solutions to understand Clio’s customers, to bring them cutting-edge AI and GenAI products, and to recommend proactive and efficient ways to serve them better
• You will play an integral role, enabling business leaders across Clio make rigorous data-driven decisions. You will help our business grow, help our customers succeed, and continuously improve the way we operate
• Collaborate with the Clio products teams to identify opportunities, develop hypotheses, and provide input that drive growth
• Identify new questions about our business, product, and customers that lead to impactful insights
• Build predictive and prescriptive AI and ML solutions and deploy them in production
• Apply rigorous statistical analysis and data mining techniques to evaluate impact of different product features and other business initiatives
• Innovate and experiment with new applications of statistical analysis, machine learning, GenAI, LLMs, etc. to unlock new product opportunities
• Build a scientific culture in product and business teams by enabling discussions with data, disseminating best practices, and leading by example
• Effectively communicate complex technical concepts and findings to both technical and non-technical audiences