Senior Statistician

About the job

Role Overview: As a Senior Statistician at Rhythm, you will be responsible for leading statistical research, designing experiments, developing predictive models, and ensuring robust data-driven insights. Your collaboration with data scientists, AI engineers, and business stakeholders will be crucial in building scalable and interpretable statistical models that support Rhythm Innovations' AI initiatives. Your expertise in statistical methodologies and machine learning will play a key role in enhancing the accuracy, reliability, and fairness of AI-driven solutions. Key Responsibilities: - Analyze customer data to identify patterns, establish correlations, and derive actionable insights to enhance AI solutions. - Develop and apply advanced statistical methodologies to analyze large-scale datasets and enhance AI model accuracy. - Design and implement rigorous experiments (e.g., A/B testing, hypothesis testing, Bayesian inference) to validate AI models and business strategies. - Work with AI and machine learning teams to integrate statistical models into AI-driven applications. - Ensure statistical integrity and robustness in AI solutions by developing methodologies for bias detection, uncertainty quantification, and explainability. - Lead research initiatives on new statistical approaches to improve AI fairness, reliability, and efficiency. - Communicate complex statistical findings to technical and non-technical stakeholders in a clear and impactful manner. - Stay current with the latest advancements in statistical methods, AI, and machine learning to continuously improve methodologies. - Mentor junior statisticians and data scientists, fostering a culture of statistical rigor and innovation. Qualification Required: - Masters or Ph.D. in Statistics, Mathematics, Data Science, or a related field. - 5+ years of experience in statistical modeling, experimental design, and data analysis, preferably in an AI-driven environment. - Proficiency in statistical programming languages such as R or Python (NumPy, SciPy, Pandas, Stats models). - Expertise in probability theory, Bayesian inference, regression modeling, time series analysis, and multivariate analysis. - Strong understanding of machine learning algorithms and their statistical underpinnings.

Requirements

• statistical modeling
• experimentation
• R
• Python
• machine learning

Qualifications

• Masters or Ph.D. in Statistics, Mathematics, Data Science, or a related field

Preferred Technologies

• statistical modeling
• experimentation
• R
• Python
• machine learning