Actuarial Data Analyst

Key Accountabilities/Deliverables

• Execute analysis on data to identify and correct data issues.
• Extracting data from various sources and transforming that data to perform analysis and create actionable visualizations.
• Live our Values: Embrace a “bring-it-on” attitude to attack issues and questions within the dept and across the organization.
• Standardize ad hoc processes and drive continuous improvement for automating and optimizing performance of ongoing processes, including development of data processing pipelines.
• Help Others Succeed: Act as a key contributor in resolving complex or expediated requests, where end-to-end ownership and analytical skills are needed to provide a solution.

Technical Knowledge and Understanding

• Intellectual curiosity: Seeking understanding of the answers to, whys of data. Uncovering patterns in data and visuals that some might take for granted and researching. Looking for trends and outliers that may be actionable.
• Openness to grow and change: The Company is in its infancy regarding our data environment and our analytics platform. Will need to be agile, willing to learn new techniques and tools with us as we grow.
• Candor: Openness to providing and receiving constructive feedback to facilitate both individual development as well as elevating the overall capabilities of the Company.
• Communication skills: Willingness to ask questions to gain understanding; collaborate effectively both with direct colleagues as well as those in other functional areas; ability to influence and gain buy-in from key stakeholders.

Experience

• Bachelor’s degree in an analytical, or related, field: Management Information Systems, Mathematics, Statistics, Computer Science, Actuarial Science and the like.
• 1-3 years of experience in a professional environment working with data, analytics.
• Functional knowledge, aptitude and understanding of basic data/analytics principles: One to many relationships, many to many relationships, reuse and linkage, data quality, validation data modeling and more.
• Experience with/or working knowledge of Lakehouse, Warehouse, Semantic models, Datamarts, SQL Databases and other relational data containers.
• Experience and/or aptitude in a Microsoft Fabric environment and the tool set included: Python, PySpark, SQL, Dataflow gen 2, Pipelines, Apache Spark jobs, One Lake, Power BI, Data agents, prompt engineering.
• Ability to convert raw data into usable and visualized representations that convey understanding and/or enables accurate execution of needed action.

Pay

The expected pay range for the role is $70,000 - $75,000. The specific offer will depend on an applicant’s skills and experiences.