At LinkedIn our approach to flexible work is centered on trust and optimized for culture connection clarity and the evolving needs of our business. The work location of this role is hybrid meaning it will be performed both from home and from a LinkedIn office on select days as determined by the business needs of the team.
This role will be based in LinkedIns 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 Operations 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:
Bachelors 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 $95000 to $158000. 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 Information :
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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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Pursuant to the San Francisco Fair Chance Ordinance LinkedIn will consider for employment qualified applicants with arrest and conviction records.
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No
Employment Type :
Full-time
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