Our Data Scientists are passionate about developing industrialized solutions and tackling complex challenges.
Data Science is a challenging field, with ever evolving methodologies and technological advancements. Our team stays on top of these changes, always keeping an eye on adapting to new business needs.
With our machine learning expertises, highly skilled and motivated data experts combined with a unique collaboration methodology and a ‘product first’ mindset, our Data Science team will help you solve your most challenging problems.
We solve problems.
How can you improve your customer lifetime value? Better understand a customer journey? How do you predict the movement of a brand new product or find new consumer trends into several millions of social networks posts?
Our data scientists have a proven track record of problem solving issues for several large companies, across diverse sectors. We work with retail, luxury, financial services, pharmaceuticals, private equity or even telecommunication companies, to leverage machine learning and analytics to create impactful solutions for our clients.
We are pragmatic and results focused engineers: we infuse our work with state-of-the-art algorithms keeping as priority the ease of implementation and short term returns on investment.
Louise, Data Scientist
At Artefact, we have direct interactions with the end-user of the solutions we implement. This allows us to do data science not just for the beauty of it, but to answer real needs. Getting immediate feedback on the added value it can bring, the challenge that needs to be answered, and how your product is being used allows you to really focus on what matters and to develop a solution that will be of help to the user.”
We work in feature teams to break silos.
In most organisations, data science teams work in silos. Their services do not scale across the full value chain and, all too often, they create ‘black box’ solutions that very few people can understand and maintain.
At Artefact, we break these silos to reach common business goals. Our data scientists work collaboratively, in feature teams, alongside stakeholders such as business owners, software engineers, DevOps, and UX designers to ensure all objectives and priorities are taken into account.
Working with Product Owners, Software Engineers and fellow Data Scientists is a truly enriching experience. Responsibilities within the team are much clearer, meaning that the Data Scientists can free a lot more time to focus on technical tasks, while staying updated about all aspects of the project. Respecting agile methodology best practices also provides more structure, ensuring that we always prioritize what will yield the most value.
Paul, Data Scientist
We think “Product” first!
We do not stop at the POC (proof of concept) stage, we always go further until industrialisation and deliver impactful and resilient products.
Our Data Scientists are committed to delivering industrialised softwares, deploying a valuable and reliable solution is our first priority, well before fine-tuning our AI algorithms. Building sound foundations in our project allows us to deploy seamlessly and quickly new features for an increased value.
Karim, Data Scientist
There are usually many possible innovative solutions to a problem, finding the one that is optimal in the context of our clients’ needs, constraints and technical stacks is where the subtlety lies. We usually go further than the proof of concept in an isolated environment. Putting a model into production is a complex task that demands best practices in MLOps, rigorous monitoring and evaluation in order to ensure and maintain the best level of performance while addressing the technical and ethical implications.
Data science sits at the intersection of applied domain knowledge, mathematics, statistics and computer science.
To nurture our R&D effort, better answer our client needs and apply the latest AI advancements in our projects, we have created, at Artefact, a set of task forces specialized into each machine learning subfield.
What is the life of a Data Scientist at Artefact ?
Working on complex and challenging missions
From supply chain to customer services, our data scientists have been working on various challenging subjects: predicting volume of calls in call centers, automating responses to customers’ requests, detecting beauty or luxury consumption trends or even helping physicians detect cancerous cells in X-rays.
Working at Artefact is also an opportunity for tech savvy engineers to develop their business understanding and better grasp the subtleties of most major industries. All our data scientists are in charge of crafting tailor made solutions that will answer very specialized business challenges and are working hand in hand with our c-level clients to foster adoption and embed business logic into smart AI products.
One of the most exciting things about working at Artefact is the variety of subjects we tackle. The term data science can cover a wide range of skills in the domains of natural language processing, forecasting or optimization to mention only a few, so each new mission brings its share of new algorithms to test and new technologies to experiment with. But picking the right model for the job is not the only responsibility of a data scientist: we need a very good understanding of the business stakes to know where we can bring the most value, which means we work closely with consultants and with our client. In order for our work to be useful in the long term, we also coordinate with software engineers and apply software best practices to turn our insights into a product.
Ombeline, Data Scientist
A dedicated training track to help our teams grow
Data science is a constantly changing field and we have our heart set on continuously training our engineers.
Data Scientists at Artefact can benefit from a large set of internal and external training, chosen carefully by our training department, helping them reach the technological frontier.
– Machine learning trainings (NLP, Forecasting, Computer Vision, ML Operations…)
– Access to Cloud certifications (GCP, Azure, AWS)
– Soft skills trainings (Oral and written presentation, negotiation, project management)
Training does not stop there: a typical data scientist’s week is infused with opportunities to learn. As we like to say “Feedback is a gift” and our culture is built around tech events such as our TechTex where we share our latest projects successes and failures or our Code Base Committee (CBC) where our projects code is challenged by our guru coders!
A tech company within a consulting company
Artefact is a consulting company, but the DS team is first and foremost a tech department:
– We leverage the latest model and ML libraries such as Sklearn, FastAI, CatBoost, Prophet, Spacy, BERT and its variants (CamemBERT, DistilBERT, …) and many more
– We are multi cloud and certified premium customer in the biggest clouds such as GCP, Azure or AWS
– We build AI products leveraging ML Ops frameworks and utilities such as Docker, Kubernetes, Kedro, ML Flow, Great Expectations, and so many more!
We promote R&D within our teams in order to stay up to date with the latest releases in the tech world.
Robin, Staff Engineer
Our field has been in constant evolution in recent years, with new algorithms, methods and implementations. Keeping up to date in this ever-changing ecosystem can be a daunting task if you’re alone. Therefore, continuous training as a team is an essential part of our life at Artefact, either through internal projects where they can try the latest tech in a problem they encounter everyday, or dedicated time during our monthly training days. Allowing our Data Scientists to continue learning on cutting-edge subjects ensures that we maintain their curiosity, but also partly their well-being within the team.
Versterk ons team
Our team has a proven track record of missions involving propensity modeling and recommendation systems. Contact us and quickly get in touch with one of our experts should you wish to know more about our expertises.
If you’d like to join us, please follow our careers webpage .