Data Analytics Senior Consultant I - Remote

Allstate

1d•Remote

About The Position

At Allstate, great things happen when our people work together to protect families and their belongings from life’s uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers’ evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection. Job Description The Data Analytics Senior Consultant I will develop, maintain, and continuously improve predictive models and operational forecasts for Claims demand and capacity, ensuring accurate and consistent outputs. The analyst will ensure integrity by implementing systematic improvements that automate, streamline, and modernize the process, data visualization, and business intelligence to ease the communication of one source of truth for data across the Claims Workflow organization.

Requirements

Experience building and implementing short and long-term demand and capacity forecasting models.
Experience with predictive staffing models preferred.
Advanced Python for data analysis and automation (pandas).
2 years of hands-on data reporting.
Advanced SQL (joins, aggregations, window functions).
Experience working with large or complex datasets.
Experience troubleshooting and debugging analytical processes.

Responsibilities

Design, build, populate, and own medium to high complexity staffing models.
Ideate and implement creative forecasting concepts to gain efficiencies, best depict information, and craft action plans to improve overall business processes.
Collaboration and communication with stakeholders, product owners and scrum team members.
Provide high quality forecasts to support stakeholders' performance initiatives.
Build and support Python workflows for operational forecasting and reporting.
Query and transform datasets using Python & SQL to create modeling datasets.
Perform data validation, reconciliation, and anomaly detection.
Monitor and maintain predictive models, including reruns and backfills.
Automate manual and spreadsheet-based processes.
Translate business questions into scalable analytical solutions.
Peer-to-peer cross training.