• 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.

Requirements Required Qualifications:

• Master’s 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.

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