My two main responsibilities at aiQ are data generation for aiQ’s POS datasets, and consulting services for institutional investors. In the aiQ POS data business, my responsibilities encompass data engineering and analysis. In data engineering, I manage POS and receipt data, performing tasks such as cleansing, formatting, and transforming the data into investment information. On the analysis front, I develop sales forecasting models for listed companies and generate client reports based on the outcomes. The tangible results of these efforts provide a clear sense of accomplishment, making each task rewarding. In my consulting role for institutional investors, I am mainly responsible for evaluating new data sets, as well as proposing and implementing investment strategies. The daily exposure to new data is one of the most thrilling aspects of being a data scientist. Simultaneously, crafting investment strategies by leveraging aiQ's domain expertise, machine learning techniques, and adept data handling skills is one of the most exciting aspects of my daily responsibilities. Furthermore, the most attractive characteristic of aiQ is the expertise of its team members. A cadre of specialists spanning quant, machine learning, derivatives, risk management, ML engineers, and infrastructure engineers collaboratively pool their knowledge to propel projects forward. The flat organisational structure unique to a venture company is also an attractive feature, allowing for very quick decision-making and direct communication with the necessary information and people. In addition, the company’s foundation on remote working enables flexible work arrangements, taking into account childcare and family circumstances. Thanks to this environment I am able to maintain a strong work-life balance