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.
• 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