Lead Data Scientist, Dynatrace

• This position requires someone comfortable with independently managing challenging projects and guiding others through technical decision-making
• A degree in Engineering, Computer Science, Mathematics, or another quantitative field
• 10+ years of demonstrable/ tenured experience in data/ data science, including at least 3 years in a lead or senior IC role
• Expertise in causal analysis methods (e.g., propensity score matching, A/B testing, uplift modeling) with a demonstrated ability to analyse tabular data
• Strong experience in Python (including Pandas, NumPy, and Scikit-Learn) for data processing and machine learning model construction
• Proficiency in SQL, with the ability to write complex queries and optimise data retrieval from relational databases
• Strong communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders

Desirable

• Our Business Insights team is seeking a Lead Data Scientist to drive impactful data-driven decision-making across our products and operations
• In this role, you’ll leverage your advanced expertise to design and develop machine learning solutions, uncover the causal relationships in complex systems, and help our customers better understand how their clients interact with their applications
• Explore and analyse millions of rows of tabular data to uncover meaningful insights and build advanced machine-learning models
• Design and implement causal analysis models to assess the impact of system performance on user experience, providing clear, actionable insights into customer behaviour
• Develop and deploy machine learning models and workflows, transforming terabytes of traffic data into actionable insights that drive key business decisions
• Lead the development and deployment of models, ensuring robustness, scalability, and reliability in production environments
• Build automated solutions for business needs, such as bot detection using advanced machine learning and statistical methods
• Collaborate closely with product owners, engineers, and other stakeholders to translate analytical findings into impactful features and product improvements
• Take ownership of technical direction, contribute to architectural decisions, identify technical debt, and advocate for opportunities for improvement
• Mentor and support junior data scientists, enhancing team productivity, improving code quality, and fostering a culture of collaboration and learning
• Continuously monitor and enhance model performance in partnership with the engineering team, improving model impact on user experience and system effectiveness