Key Responsibilities :

- Collaborate with business stakeholders to identify opportunities for leveraging data and AI to drive business solutions in the insurance space.

- Design and implement forecasting models, predictive models, classification systems, and deep learning solutions tailored to insurance-specific use cases.

- Own the end-to-end delivery of data science projects from data exploration and model development to validation, deployment, and monitoring.

- Work with large and complex insurance datasets to extract meaningful patterns and drive decisions.

- Apply advanced statistical techniques, machine learning algorithms, and deep learning architectures to solve real-world business problems.

- Collaborate with data engineers and DevOps teams to productionize ML models and scale solutions on cloud platforms like Azure or AWS.

- Maintain awareness of the latest AI/ML tools, techniques, and industry trends to keep solutions and skills up-to-date.

- Mentor junior team members and provide technical leadership where needed.

- Build dashboards and reports to visualize the performance of models using tools such as Power BI, Tableau, or other visualization platforms if required.

Skills & Competencies :

- Proven experience in delivering complex AI/ML projects within the insurance domain preferably life, general, or health insurance.

- Expert-level proficiency in R and Python, with hands-on experience in Machine Learning (XGBoost, LightGBM, Random Forest, SVM, etc.), Deep Learning (CNNs, RNNs, LSTMs, Transformers using TensorFlow, PyTorch, or Keras), Statistical modeling & forecasting (ARIMA, Prophet, Exponential Smoothing, etc.

- Strong understanding of modeling concepts, feature engineering, hyperparameter tuning, model validation, and performance evaluation metrics.

- Experience with data manipulation and transformation using pandas, dplyr, SQL, etc.

- Familiarity with cloud platforms (Azure or AWS) and their respective ML/AI services (Azure ML, Sagemaker, etc.)

- Good understanding of data engineering concepts data pipelines, ETL processes, and working with structured and unstructured data.

- Ability to articulate findings clearly to both technical and non-technical stakeholders.

- Strong problem-solving ability and attention to detail in a fast-paced, high-performance environment.

- Knowledge of data visualization tools such as Tableau, Power BI, or Plotly is a plus.

Requirements :

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.

- 5-10 years of relevant experience in data science roles, preferably in insurance or financial services.

- Certifications in Machine Learning, Deep Learning, or Cloud-based AI services (i.e., Microsoft Azure AI Engineer, AWS ML Specialty) are a plus