Senior Predictive Analytics & Statistical Modelling Specialist

Key Responsibilities:

• Develop and implement predictive models using advanced statistical techniques and machine learning algorithms.
• Perform exploratory data analysis (EDA) to identify trends, anomalies, and opportunities for optimization.
• Build, test, and validate statistical models (regression, time-series forecasting, classification, clustering, etc.) to drive decision-making.
• Collaborate with AI engineers, data scientists, and business stakeholders to translate business challenges into data-driven solutions.
• Deploy models into production environments and monitor their performance, accuracy, and scalability.
• Communicate insights effectively through visualizations, dashboards, and reports for technical and non-technical audiences.
• Continuously explore new methods, tools, and algorithms in AI, ML, and predictive analytics
  
  to improve outcomes.
  

Required Skills & Qualifications:

• Education: Bachelors or Masters in Statistics, Mathematics, Computer Science, Data Science, or related field.
• Experience: 4+ years in predictive analytics, statistical modelling, or data science roles (preferably in AI or tech-driven companies).
• Strong knowledge of statistical techniques (regression, hypothesis testing, probability, time-series, multivariate analysis).
• Proficiency in Python / R for modelling, data manipulation, and visualization.
• Experience with ML frameworks (scikit-learn, TensorFlow, PyTorch) and data handling tools (SQL, Pandas, NumPy).
• Exposure to cloud platforms (AWS, GCP, or Azure) for model deployment.

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  Strong problem-solving skills with the ability to work on structured and unstructured datasets.
• Excellent communication skills to explain complex analytical results to business teams.
  

May Have

• Experience in natural language processing (NLP), recommendation systems, or deep learning.
• Knowledge of MLOps practices for scalable AI model deployment.
• Familiarity with big data ecosystems (Spark, Hadoop, Databricks).
• Publications, certifications, or contributions to AI/ML research communities