Senior/Lead Data Scientist

Responsibilities

• Collaborate with cross-functional teams and stakeholders to identify business requirements and objectives, manage deliverables against timelines, present of results and implement the model.
• Utilize advanced statistical analysis techniques to perform hypothesis testing, A/B testing, and predictive modeling.
• Stay abreast of the developments in the rapidly changing field of AI and machine learning.
• Mentor and coach junior team members and provide support on projects requiring advanced skill and domain expertise.
• Gain deeper understanding of Chubb's business and develop expertise on multiple lines of business.
• Use broad spectrum of NLP, Machine Learning, and deep learning techniques to extract impactful features from unstructured data.
• Develop and implement models that help with drawing risk related insights from large datasets to aid business decision making; Ensure that the model can meet the desired KPIs postproduction. Develop metrics to measure business performance and monitor the effectiveness of data-driven initiatives.
• Collaborate with IT teams to optimize data collection, storage, and retrieval processes to support efficient and reliable data analytics.

Requirements

• Sound understanding of Generative AI based models and Prompt Engineering and LLMs to drive innovation and generate valuable insights from the data.
• Strong grasp of foundational ML concepts, including Probabilistic Models, Supervised vs Unsupervised Learning, Ensemble Techniques, Hyperparameter Optimization, and Deep Learning.
• Good knowledge in machine learning tools and libraries (matplotlib, ggplot, scikit-learn, h2o, MLlib, etc.)
• Proficient in Python with strong capabilities in data engineering, data transformation, data analysis and data dashboard building. Knowledge of R/SQL would be an add-on.
• Strong understanding of BERT and Transformers models is highly desired for this position.
• Bachelor's degree in computer science, Statistics, Math, Actuarial, or related quantitative fields. Advanced degree in data science, business analytics would be an add on.
• Minimum 6 years of hands-on experience in data analytics fields would be good.

Previous exposure to/ knowledge of the insurance industry will be an added advantage.