Senior Analytics Specialist

About the Team:

In this role, you will be part of the GDM Analytics team, with responsibility to support all efforts to enhance our team's sales data analytics capabilities. You will be required to coordinate our efforts and actions with the nine market units. Keeping constant client and broker focus with particular emphasis on our growth initiatives and sales campaigns will be important.

About Role:

Minimum qualifications:

• Bachelor's, Master’s or PHD in Statistics, Mathematics, Data Science, Computer Science or a related quantitative field.
• At least 5 years of work experience with analysis applications (e.g., extracting insights, performing statistical analysis, or solving business problems), and coding (e.g., Python, R, SQL).
• Commercial Insurance experience would be a plus.

Job Requirements:

• Work with stakeholders throughout the GDM organization to identify opportunities for leveraging data to drive business solutions.
• Mine and analyze data from Sales Analytics databases to drive efficiency improvements and meet growth objectives.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques.
• Develop custom data models and algorithms to apply to data sets.
• Develop and automate reports, iteratively build dashboards to provide insights at scale, solving for business priorities.
• Develop processes and tools to monitor and analyze model performance and data accuracy.
• Collaborate with cross-functional teams to understand business requirements and identify opportunities for applying GenAI technologies
• Stay up to date with the latest advancements in GenAI technologies and recommend innovative solutions to enhance data science processes.
• Deliver effective presentations of findings and recommendations to multiple levels of stakeholders, creating visual displays of quantitative information. Manage stakeholders, solicit ideas, prioritize, and manage expectations.

 About You: 

• Strong problem-solving skills with an emphasis on solution development.
• Experience with analysis applications (e.g., extracting insights, performing exploratory data analysis, or solving business problems), and coding (e.g., Python, R, SQL) to manipulate data and draw insights from large datasets.
• Proficiency in machine learning libraries and frameworks such as TensorFlow, PySpark, scikit-learn.
• Experience with data preprocessing, feature engineering, and data wrangling techniques.
• Solid understanding of probability concepts, statistical analysis, hypothesis testing, and experimental design.
• Experience with Large Language and other Machine Learning models to analyze structured and unstructured data (e.g., for Natural Language Processing).
• Knowledge of a variety of machine learning techniques (clustering, ensembles, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Excellent written and verbal communication skills for coordinating across teams.
• A drive to learn and master new technologies and techniques.