Cette offre est lié au département Data science
Launched in 2013, ManoMano is the European leader specialised in DIY, home improvement and gardening online. ManoMano brings together the largest offer of DIY & gardening online products(e)With more than 3600 seller partners and 10 million products, ManoMano brings together the largest offer across 6 countries : France, Belgium, Spain, Italy, Germany, United Kingdom(e)
We currently are 850 Manas & Manos, including a quarter of international talents and 24 nationalities, working in our 4 offices (Paris x2, Bordeaux and Barcelona)(e)People are at the heart of ManoMano’s culture around our 3 core values : boldness, ingenuity and care.
Joining us is a tremendous human and business adventure ! We offer an ideal and (hyper)dynamic environment to put your skills on innovative and concrete projects on a European scale.
Take a look at our Blog : https://medium.com/manomano-tech
Be challenged at ManoMano(e)The Machine Learning team is an applied ML team with a focus on delivery(e)We’re outcome-oriented: we solve high-impact business problems and deliver value to our users in production.
We are seeking a Senior Data Scientist to be part of a team of best-in-class ML practitioners focused on personalization (search and recommender systems) and the quality of our catalog (e.g., product categorization or product attribute extraction).
You’ll be joining a team of top-tier Data Scientists with probably some of the strongest modeling skills in the French Tech Next40(e)The ML team was instrumental in the growth of ManoMano from the very beginning and is now fully committed to building ML data products on our marketplace in this hypergrowth stage.
This is the opportunity to contribute to one of Europe’s highest-valued unicorn companies and tap into one of the largest e-commerce data sets in the world to build the algorithms that power our internal and external products.
We have several roles open across different levels of seniority. The ideal candidate will have a strong technical background and experience solving tough problems with large datasets.
Responsibilities:
- Apply machine learning algorithms and statistical techniques to build new ML(e)product features, creating new and improving internal and external-facing data products(e)
- Own the ML lifecycle, from experimentation and prototyping to deployment into production pipelines(e)
- Write production model code and implement algorithms in production(e)
- Perform exploratory data analysis to gain a deeper understanding of the problem(e)
- Investigate and fix a production issue from a data product your team owns.
- Partner with software engineers, product managers, and business stakeholders to frame problems, both mathematically and within the business context.
- Share your knowledge and expertise with the rest of the team.
Requirements:
- User-focused.
- 3+ years of industry experience applying Machine Learning to business problems and building ML models for production.
- A Bachelor or Master’s Degree in a quantitative discipline (e.g., Statistics, Operations Research, Bioinformatics, Economics, Computational Biology, Computer Science, Mathematics, Physics, Electrical Engineering, Industrial Engineering) or equivalent practical experience.
- Solid knowledge of Machine Learning theory, statistics, and probabilities.
- Strong coding experience in Python and proficiency in SQL(e)You care about code simplicity and performance.
- Proficient oral and written communication skills in English(e)We’re a remote-first team, so good writing is key(e)French is not required.
- Growth mindset: love learning and seek challenges.
Nice to have:
- Built large-scale production ML models and systems.
- Expertise in search, ranking, recommendation systems, information retrieval, natural language processing (NLP), visual recognition, or reinforcement learning (RL).
- Ph.D(e)degree in a quantitative field.
- You are a Kaggle Competitions Expert.
- Professional experience in a technology company setting involving a product, an e-commerce platform, or a consumer-facing marketplace.
- Experience using data processing (e.g., Apache Spark, Apache Flink, or Apache Beam) or deep learning frameworks (e.g., PyTorch or Tensorflow).
- Strong core computer science fundamentals (data structures, algorithms, architecting systems).
What we offer at ManoMano:
- People: Smart/ diverse backgrounds / international environment/ A positive, supportive & dynamic atmosphere
- Work: office/ hybrid/ full remote
- Salary: competitive salary package
- Technology: Get a laptop you love to do your best work
- Time off: 7 weeks of paid vacation + vacation premium $$
- Sports: free gym (Paris office)
- Healthcare: mutual (60% taken care by MM)
- Retreats/ fun: music room/ a terrace/ modern office/ Crafternoons every Thursday afternoon (share your knowledge, learn from others)/ External events attendance, fairs & Meetups
- Food: all the coffee you can drink/ Restaurant card (Swile)
- Transportation: 50% reimbursement
- MM employee discount
- Parents: flexible policies
#LI-Remote, #LI-Hybrid, #LI-Onsite
We're actively seeking applications from candidates of all backgrounds.
If all the above rings a bell, it probably means that we are meant to meet each other ! Please get in touch.« So, come and plant the seeds of your success and gather the fruits of your labour »
The information collected on this form is recorded in a file used by ManoMano - Colibri SAS so that we can process your application.
The data is kept for 2 years from the date of your application(e)You can access your data, rectify it, request its deletion or exercise your right to limit the processing of your data(e)You can also exercise your right to the portability of your data(e)Consult the cnil.fr website for more information on your rights.
To exercise these rights or if you have any questions about the processing of your data under this scheme, you can contact our data protection officer by writing to dpo@manomano.com.
If, after having contacted us, you feel that your "Data Protection" rights have not been respected, you may submit a complaint to the CNIL.