Senior Statistician - Applied AI

Position Overview:

As a Senior Statistician, you will lead statistical research, design experiments, develop predictive models, and ensure robust data-driven insights. You will collaborate closely with data scientists, AI engineers, and business stakeholders to build scalable and interpretable statistical models that support Rhythm Innovations' AI initiatives. Your expertise in statistical methodologies and machine learning will help enhance 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 analyse 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 explainable.
  
• 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 improve methodologies continuously.
  
• Mentor junior statisticians and data scientists, fostering a culture of statistical rigor and innovation.

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Required Qualifications:

• 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 modelling, time series analysis, and multivariate analysis.
  
• Strong understanding of machine learning algorithms and their statistical underpinnings.
  
• Experience working with large-scale data and cloud-based data platforms (e.g., AWS, GCP, Azure).
  
• Proven ability to translate complex statistical concepts into actionable insights.
  
• Excellent problem-solving and critical-thinking skills.
  
• Strong communication and collaboration skills, with the ability to work in cross-functional teams.
  

Preferred Qualifications:

• Experience in AI ethics, fairness, and interpretability of machine learning models.
  
• Knowledge of causal inference and advanced experimental design techniques.
  
• Familiarity with deep learning frameworks (TensorFlow, PyTorch) and their statistical implications.
  
• Publications in peer-reviewed journals or conferences related to statistics, AI, or machine learning.