Analytics Manager

Analytics Managerspecializing inMarketing Mix Modeling (MMM)is required to join our analytics team. In this role, you will be responsible for developing, implementing, and optimizing marketing effectiveness models to drive data-informed decision-making. You will work closely with cross-functional teams, including client success and data engineering to measure the impact of various marketing channels and provide strategic recommendations that maximize ROI.

You will be expected to work across a set of clients and support Directors & Partners by taking responsibility for the day-to-day management of a project, leading a team of analysts and communicating with stakeholders to deliver a project on time to Gain Theory's high standards. You will also have strengths in networking developing relationships with counterpart’s client and agency side and be an externally facing ambassador for the Gain Theory brand.

You will also be required to take on additional tasks, examples of these are: creating marketing content, developing products & services, and supporting the development of Gain Theory analysts.

Key responsibilities include:

Working with data:Data extraction and manipulation, data analysis and validation, batch files, programming

• Be able to explain and oversee the use of data extraction tools (i.e., Advantage, AdDynamix, Sysomos, Google trends, Google analytics, Double Click, etc.)
• Ensure that ROVA inputs have been checked and is free from errors before internal meetings where it is required.
• Take responsibility for and manage data collection including preparation and sending of data requests to clients and agencies.
• Create data validation deck and oversee a process to ensure all data used is correct and signed off by a client and agencies.

Building Models:Model building, validation and signing off, media optimization

• Work with junior team members to validate models, identify areas of weakness, suggest and test possible improvements and ensure robustness and validity.
• Make sure that any applicable diagnostic tests are passed and that the outputs make sense before passing models onto the senior team.
• Be familiar with all standard data transformations, and be explain to explain the merits of each one. These include STA, SUB, DIVMDV_SUB, YTY. Ideally also include RNT and SUB_NORM (subtract mean and divide by standard deviation)
• Be familiar with all regression based options within Rova. For an MMM this includes GLS, CLS, Bayesian MMM and Hierarchical Bayesian.
• Create response curves and optimization spreadsheet or alternatively use available tools for budget allocation. This requires knowledge of internal tools such as Orca, Chasm and GTi. Oversee scenarios required to answer specific client objectives.
• Perform quality control of output, statistical modelling and integrate research insight from a wide variety of sources.
• Start taking ownership of final model selection, initially with guidance from a senior colleague, but work hard to learn the processes and improve your own model sign-off capabilities such that less guidance is required over time.
• Take ownership of final model sign-off, to be verified by analytics director.

Creation of presentations:Result interpretation and rationale, recommendations, translation of results from analytics into actionable recommendations

• Set up deck flow or support client lead in doing so and create placeholders to be populated by the team. Also being able to communicate details of expectations to the junior team.
• Check deck content ensuring it contains consultancy output rather than a series of factual statements.
• Provide input into the results and implications and comment on the interpretation for future strategies.
• Be able to explain and justify any potential changes that need to be made to provide sensible results.
• Interpret results and understand the implications of these results to the client. Be able to explain your interpretation to the team and defend your POV.
• Create draft of recommendations to the client and organize any follow-up or areas of clarification needed.

Project & Resource Management:Project management, timing plans, tasks allocation, project delivery

• Liaise with team members and external suppliers to agree on lead times for each stage of the project, oversee analyst tasks within this to meet deadlines set.
• Assign responsibilities to team members and ensure tasks are completed in the timely manner.
• Manage day-to-day operational aspects of the project using resources at your disposal to their full potential.
• Work closely with relevant stake holders to ensure effective and efficient implementation of the project and ensure our clients are delivered market leading analytics tailored to their specific needs.

Team Support & Development:Team collaboration, leadership, communication

• Assign team members with tasks that allow them to meet their personal goals and objectives.
• Assist team members in interpreting the tasks they have been set.
• Identify and acknowledge team members’ individual strengths and nurture skills to the benefit of the team.
• Ensure that any staff experiencing performance difficulties are managed appropriately and work to identify measures that could be used to improve performance.
• Identify training and development needs jointly with team members and their personal managers.
• Provide the team with a vision of the project objectives; initially support may be required from more senior members in the team.

Business Development:New business development, client retention, business development planning, management and research.

• Support client leads in achieving revenue targets.
• Support client leads in achieving revenue targets and with tasks relating to pitch material creation or internal product and services collateral / R&D etc.
• Attend conferences, meetings and industry events particularly when these are for your industry vertical or horizontal specialties.

What you’d need to succeed:

• Bachelor's or Master’s degree inStatistics, Economics, Data Science, Marketing Analytics, or a related field.
• 4+ yearsof experience inMarketing Mix Modeling (MMM), econometric modeling, or marketing analytics.
• Strong proficiency inPython, R, SQL, or similar analytical tools.
• Experience working withlarge datasetsand conducting statistical analyses.
• Knowledge ofBayesian modeling, machine learning, or AI-based MMM approachesis a plus.
• Ability to work withcross-functional teamsand translate analytical insights into business strategies.
• Excellent communication skills with the ability to present findings to both technical and non-technical stakeholders.
• Strong project management skills and the ability to prioritize tasks in a fast-paced environment.