Join Qonto’s data team!

The economy has gone digital, and that means that an increasing amount of information about prospects, clients, competitors, etc. is being collected and stored on companies’ computer systems. We call this “Big Data”.

But this data doesn’t just sit around gathering dust- we need to find a way to leverage this data and extract valuable insights from it to support business growth. This is where the Data Scientist comes in.

They have a special set of skills to help weed out irrelevant information and repackage the remaining pertinent data into precious strategic elements to be used by various departments within the company.

Data Scientists sort through every data channel:

1. Omnichannel data collection;
2. Data cleanup;
3. Data indexing and consolidation for better use;
4. Figuring out patterns to make predictions;
5. Creating dashboards to help managers with decision-making.

At the heart of the “Big Data” team sits the Data Analyst. Their job is to make sense of the labyrinth of data that a company collects. That way, Management gets to make better, more informed decisions, and the data reports can help direct marketing strategy.

Here are some of the tasks a Data Analyst would be expected to perform:

1. Extract the key information from what the Data Engineer has collected;
2. Centralize said data in a dedicated space for easy access;
3. Outline the KPIs and build dashboards to track performance;
4. Develop automated processes;
5. Give recommendations to different team managers.

The big challenge for a Data Analyst is creating new tools to effectively sift through the mountain of information a company collects. Most of it sits unused to this day.

This is why, to make the most of this information, it’s essential for a Data Analyst to have excellent statistical skills and master software and programming languages like SAS, SQL, VBA, Access or R.

Data Engineers work in tandem with Data Scientists by building the framework that collects information for the Data Scientist to examine. They’re effectively the first ones to come into contact with the data in the company.

The challenge here is to mine the desired information from the pool without compromising on security. Every day, they have to monitor the data treatment systems for quality and relevance.

Data Engineers also do a fair amount of cleanup. Extraneous data needlessly burdens the servers and can afford to be removed.

To do all this, a Data Engineer has to master certain technical skills, such as:

1. Database architecture;
2. Modelling tools;
3. Coding, specifically Python, Java, and C/C++;
4. SQL and NoSQL technology;
5. AI.

Similarly to Data Scientists, Machine Learning Engineers are top data processors and can build complex models for handling active data.

Their role in the Tech & Growth team consists of implementing automated predictive models with the help of “self-running” software. Machine Learning depends on the machine’s ability to “learn” from previous operations so it can fine-tune its forecasts.

Using this technology, Machine Learning Engineers can consistently improve algorithms while eliminating human error in the process.

Machine Learning Engineers work closely with various members of the Tech & Growth team such as Data Scientists, Software Engineers and Data Engineers, but also with UX/UI specialists in the Product & Design department.