In less than a decade, ManoMano has become a key player in the home improvement and renovation sector.
Launched in 2013, ManoMano is the reference online marketplace for DIY, home improvement and gardening. Co-founded by Philippe de Chanville and Christian Raisson, ManoMano brings together the largest offer of DIY & gardening online products: electricity, plumbing, hardware, frames, indoor and outdoor furniture, tools, etc. With more than 2 600 seller partners and 7,5 million products, ManoMano currently employs 600 people and operates in 6 markets (France, Belgium, Spain, Italy, Germany, United Kingdom).
Motivated by the prospect of improving the living environment of their customers and convinced of the importance of the home market for sustainable consumption habits, the ManoMano teams want to help write a new page in their industry, which is struggling to reform itself. ManoMano brings to a highly technical world the power of its sector expertise, combined with that of data and digital in all its dimensions, to offer our customers easy access to innovative advice, products and services 100% online.
The ambition of the Founders and, above all, of Manas & Manos? To accompany this sector transformation with a strong culture of boldness, in an ingenious and frugal organization that places people and teams at the heart of the company's development.
Be challenged at ManoMano!
The Machine Learning team is an applied ML team of 14 people with a focus on delivery. We’re outcome-oriented: we solve high-impact business problems and strive to deliver value to our customers.
We are seeking a Senior Data Scientist for our Paris office, to be part of of team of ML practitioners focused on operations (e.g.: delivery time estimation, efficient warehouse management via sales forecasts), revenue optimization (sponsored Ads optimization, recommender systems, …), catalog management (e.g., product categorization or product attribute extraction) or the internal search engine.
The ML team is instrumental in the growth of ManoMano and is now fully committed to building ML data products on our marketplace.
If you wish to know more about what we do :
How do we forecast delivery times at MM
How do we leverage LLMs for attribute extraction
Our take on how to tackle position bias
- Leverage machine learning and statistics to build predictive models tailored to revenue optimization, customer acquisition, recommender systems, etc.
- Write production-ready code and deploy algorithms in a live environment.
- Carry out A/B test on a large B2C and B2B customer base
- Investigate and fix production issues.
- Partner with software engineers, product managers, and business stakeholders to frame problems, both from a scientific and business point of view.
- Be actively involved in technology watch regarding the latest data science trends (Generative AI, scalable ML,...)
Technical stack :
Python
AWS
Airflow
Kubernetes
Gitlab
Snowflake
User-focused.
3 to 8 years of experience in a Data Science/ML engineering role
Master’s Degree / Ph.D. in a quantitative discipline (e.g. Statistics, Operations Research, Computer Science).
Experience deploying machine learning algorithms at scale
Solid knowledge of Machine Learning theory, statistics, and probabilities.
Strong coding experience in Python and proficiency in SQL. You care about code simplicity and performance.
Proficient oral and written communication skills in English.
Willingness and ability to debug production issues
Growth mindset: Always striving to improve your technical and soft skills.
Good knowledge of deep learning and NLP frameworks (PyTorch, Transformers, …)
Experience implementing and analyzing A/B tests
Some familiarity with Bayesian inference
Familiarity with Streamlit and MLOps practices.
Knowledge of scalable processing frameworks (Dask, Ray, etc.)
Part-time remote option (max 2 days per week)
Flexible working hours
Health care coverage
Meal Voucher: Swile Card
Employee discount on our DIY & HI offering
Take care of your mental health with our dedicated partner with moka.care
Free access to a gym in Paris
Online coding quiz focused on your Python, SQL, and ML chops.
Introductory call with a talent acquisition manager to get a feel for your motivations and talk about the role.
A call with the hiring manager to assess your fit with the data science team and address any questions you might have (30-45’).
A take-home technical assignment to assess your ML, data processing, and programming skills. The take-home should take 3 to 4 hours for an experienced Data Scientist.
On-site or virtual interview with a Senior and a Lead Data Scientist (2h): this is the opportunity to discuss your take-home assignment and go over a few of our internal use cases: we assess your critical thinking, knowledge of statistics and ML, and pragmatism.
Apply now and join an exciting adventure ! ✨
You are welcome to apply to ManoMano, regardless of your gender, religion, age, sexual orientation, ethnicity, disability.
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