This role will be based in LinkedIn’s Omaha, NE office.

The Senior Forecasting Analyst for Trust Review Operations (TRO) will support the budget planning, forecasting, and analysis processes, providing insights and recommendations to drive Trust Operation’s financial performance and strategic decisions. Candidates will have a strong understand of and ability to apply analytical principles such as correlation analysis, cohort analysis, & predictive modeling

Key Responsibilities:

• Analyze large datasets to identify trends, patterns, and insights to drive adjustments to models, develop reporting, identify opportunities, and provide recommendations to enable decision making.
• Use statistical techniques to develop, implement, and maintain predictive models to forecast operational and financial performance.
• Monitor model performance and drive continuous improvement to models based on changes in trends, products, business decisions, and feedback.
• Prepare detailed operational & budget analysis, variance analysis, and reporting for leadership on weekly, monthly, quarterly, and annual basis.
• Collaborate with cross-functional teams to define business problems and change impact to develop data-driven solutions, managing within weekly capacity plans and budget constraints.
• Present findings and insights to stakeholders in a clear and actionable manner.
• Support ad-hoc analysis and special projects as needed.
• Stay current with industry trends and advancements in analytics, statistics, and predictive modeling.

Qualifications Basic Qualifications:

• Bachelor’s in Statistics, Data Science, Computer Science, Mathematics, Economics, or related technical field or equivalent practical experience
• Experience in predictive analytics, data analysis, and statistical modeling.
• Experience in programming languages such as Python, R, or SQL.
• Experience with data visualization tools (e.g., Tableau, Power BI).

Preferred Qualifications:

• MBA or Masters in Statistics, Data Science, Computer Science, Mathematics, or Economics
• Certification in data science or mathematical fields.
• Experience with Workforce Management systems (e.g., NICE).
• Familiarity with ETL process and data warehousing techniques.
• Strong attention to detail and accuracy.
• Excellent communication skills, with the ability to communicate complex analytical information clearly and effectively.
• Strong organizational and time management skills.
• Ability to work independently and as part of a team in a fast-paced environment.
• Excellent problem-solving, critical thinking, and analytical abilities
• Familiarity with big data technologies (e.g., Hadoop, Spark) and data analytics tools.

Suggested Skills:

• Budget forecasting and planning
• Workforce Management Planning
• Data analysis
• Data visualization

LinkedIn is committed to fair and equitable compensation practices.

The pay range for this role is $95,000 to $158,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.

The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linkedin.com/benefits.

Additional Information Equal Opportunity Statement We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.

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• Documents in alternate formats or read aloud to you
• Having interviews in an accessible location
• Being accompanied by a service dog
• Having a sign language interpreter present for the interview

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