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Operational Planning Analyst / Data Modelling
Pearson
London, , United Kingdom
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Description
About Pearson
At Pearson, we’re committed to a world that’s always learning and to our talented team who makes it all possible. From bringing lectures vividly to life to turning textbooks into laptop lessons, we are always re-examining the way people learn best, whether it’s one child in our own backyard or an education community across the globe. We are bold thinkers and standout innovators who motivate each other to explore new frontiers in an environment that supports and inspires us to always be better. By pushing the boundaries of technology — and each other to surpass these boundaries — we create seeds of learning that become the catalyst for the world’s innovations, personal and global, large and small.
Context
This is a data-oriented team which sits within Solution Delivery in Technology Operations. The objective of the team is to statistically model out resource demand across different Tech Ops teams, identify bottlenecks in workflow, reengineer business processes to improve efficiency and drive data-based decision making in operational planning.
Summary
The Analyst is responsible for monitoring, managing, and forecasting incoming TechOps customer demand. They will create and maintains forecast models on workload, costing and capacity requirement, and provide internal and external reporting against key performance indicators. An important part of the role will also be to monitor and analyse the success of the underlying process, identify bottlenecks and areas for simplification and improvement and plan and drive implementation of changes.
The Data Modeling Analyst will work with different teams across Technology Operations to build forecast for various dynamics of workload inflow, as well as re-engineer business process to facilitate data collection .
The role reports to the Operational Planning Manager and sits within resource modelling and process improvement team, objective of the team is to statistically model out resource demand across different Tech Ops teams, identify bottlenecks in workflow, reengineer business processes to improve efficiency and drive data based decision making.
Responsibilities
• Gather and validate data TechOps pipeline, Service Now and other tools or databases applicable
• Manipulate and link different data sets as required
• Review historical demand trends, research demand drivers, prepare forecast data, develop statistical forecast models, and evaluate forecast results
• Coordinate cross-functional research and data-gathering activities with partner teams to aggregate demand and provide feedback on overall flow
• Review product and technology road maps to identify significant future demand drivers and inform overall forecast models
• Perform routine statistical analyses and ad-hoc queries
• Use a range of analytical techniques such as data mining, time series forecasting and modelling techniques to identify and predict trends and patterns in data
• Prepare and maintain dashboarding and reporting for pipeline as well as other demand planning process
• Identify bottlenecks in the process using KPI data
• Translate pipeline/Service Now data or other data sets into management insight and facilitate decision making
• Analyse the underlying process, create, gain agreement on and implement continuous improvement plans
Qualifications
• BA in Mathematics, Statistics, Business, Economics, or equivalent combination of education and experience, required
• 2 years of relevant working experience in data analysis, data mining and statistics
• Knowledge of project management and business process mapping
• Detailed orientated approach to tasks with focus on accuracy and ability to add insights
• Good knowledge of excel is essential, additional knowledge of SPSS, R or Python is preferred.
• Strong analytical and computer skills
• Strong written and verbal presentation skills and ability to communicate well with senior management with minimal guidance
• Enthusiastic approach to problem solving
• Comfortable with ambiguity and have a trailblazer mentality