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Data Science & AI, Product management

Data Product Manager vs Data Scientist: what are the differences?

Today, data plays a key role in decision-making, and two professions stand out for their ability to exploit it effectively: the Data Scientist and the Data Product Manager. Although their missions sometimes overlap, their approaches and responsibilities are distinct. To better understand the difference between these roles, let's imagine a car. The Data Scientist is the mechanic who adjusts the engine, optimising the predictive models. The Data Product Manager, on the other hand, is the driver who sets the direction by ensuring that the product is aligned with business needs. Together, they ensure that the vehicle moves efficiently towards its objective.

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The role of the Data Scientist: exploring and modelling data

The Data Scientist is an expert in data analysis and modelling. Their main role is to transform massive volumes of information into actionable insights using tools such as Python, R, TensorFlow or scikit-learn. He develops predictive models to identify trends, anticipate behaviour and optimise processes.

As well as building models, they must also make their analyses accessible to business teams so that they can be used in decision-making. For example, a Data Scientist in the retail sector can design a model that anticipates stock shortages based on purchasing trends and seasonal cycles.

The role of the Data Product Manager: defining and managing data products

The Data Product Manager is responsible for the vision and strategy of a data-driven product. Their objective is to ensure that data projects meet concrete business needs and create real added value. He or she works closely with the Data Scientists, Data Engineers and stakeholders to align technical developments with the company's strategic objectives.

Their role does not stop at project management: they must also ensure that the solutions developed are adopted and used effectively. For example, in a bank, a Data Product Manager might lead the development of an AI-based credit risk assessment tool, defining product requirements, aligning technical teams and monitoring performance after launch.

Distinct but complementary roles

The Data Scientist and the Data Product Manager are two key players in the data transformation process, but they have different priorities. The Data Scientist focuses on developing high-performance, robust models, while the Data Product Manager ensures that these models are integrated into a product that meets user expectations and business challenges.

In a product recommendation application on an e-commerce site, for example, the Data Scientist designs the algorithm that generates personalised recommendations. The Data Product Manager ensures that these recommendations are put forward, that they are relevant to the user and that they contribute to the commercial objectives.

What are the fundamental differences between these two professions?

The Data Scientist focuses on data analysis and modelling, using advanced algorithms and statistical approaches. The Data Product Manager, on the other hand, adopts a strategic and business perspective by defining the product vision and ensuring its impact and adoption.

The former is concerned with "how to analyse and exploit data", while the latter is thinking about "why and for what purpose to use this data".

Essential collaboration to maximise the impact of data

A predictive model, however effective, will have no impact if it is not integrated into a usable and relevant product. Conversely, a well-defined product vision cannot be achieved without reliable, high-performance models.

The Data Scientist and the Data Product Manager are therefore two complementary players. One explores and models, the other guides and transforms these models into strategic solutions. It is this collaboration that enables companies to fully exploit the power of their data.

The success of data projects depends on effective synergy between Data Scientists and Data Product Managers. While the former develops the models, the latter ensures that they are integrated and adopted.

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