London, England, United Kingdom
Game monetisation is the process through which a gaming company generates revenue from its games. At Product Madness, we monetise our games by selling virtual coins and objects to our players (in-app purchases), and showing them ads from our partners.
Ad monetisation data science aims to describe and predict players behaviour toward ads to help Product Madness grow its ads revenue whilst preserving players experience. You will articulate, model, and test assumptions to describe the relationships between ads and the overall game economy.
How can we grow our ads revenue whilst preserving user experience? How can we predict whether or not a user will make a purchase? How can we segment our users with respect to their behaviour towards ads? How can we estimate the revenue we will generate from introducing a new type of ads?
These are just a few of the questions you would be answering as an ad monetisation data scientist. To do that, you will develop and produce machine learning models, analyse data to answer business questions, run AB tests to test your propositions, create performance indicators and dashboards to support decision making across the business, and communicate your insights to other data scientists and stakeholders.
Skills and Requirements
You have a passion for quantitative analysis and will have the ability to draw business insights which will add real value to Product Madness. You will need to demonstrate the ability to learn quickly and will have worked in a fast-paced and collaborative environment. This role will involve working with teams and individuals across the company - such as Product Directors and Managers, Data Scientists, Business Performance and CRM managers, so excellent communication skills are essential.
Required skills and experience:
- Degree in a relevant field required (Economics, Operations Research, Math, Physics, or other quantitative science majors - PhD in any of the above is a plus).
- Analytical coding: Using tools such as Python, R, Matlab (and sometimes Excel) for analytical purposes
- Good working knowledge of SQL
- Experience in data analysis and modelling
- Strong understanding of machine learning
- Highly analytical and structured thinking
Specific experience that might be helpful includes:
- Experience in the gaming industry or similarly sophisticated customer-facing digital businesses;
- Robust understanding of more advanced statistical techniques suitable for analysis of highly skewed populations;
- Experience productionising models in a cloud environment (such as AWS);
- Experience in predictive analytics, segmentation, and related areas; experience in experimental design;
- Skills with standard reporting tools such as Looker .
- Up to 5% employer pension contribution with Aviva
- Private Health insurance with Vitality
- Full dental insurance
- Life Insurance
- Long term disability insurance
- 25 days’ holiday
- Employee assistance programme
- Bonus scheme
- Season ticket loan
- Personal development & career plans
- Relocation support
- A rated visa sponsor
- Study assistance program
- Moving house day