- Date Posted
- Jun. 28, 2021
- Data Science
BlaBlaCar is the world’s leading carpooling platform, created with one dream in mind: leveraging technology to fill the millions of empty seats on the road. We offer long- and short-distance carpooling as well as a bus marketplace, with the mission to become the go-to marketplace for shared road mobility.
Today, our community counts over 90 million travelers in 22 countries, creating a smarter, friendlier and carbon-saving transport network. Every year, our community saves 1.6 million tons of CO2e by sharing the road, equivalent to the CO2 emissions generated by Paris traffic in a year. But it doesn’t stop here – our team of 250+ engineers is developing innovative algorithms to further unlock the potential of shared travel and multiply its impact.
We’re looking for people to join our journey – people who care, who are driven by impact and innovation, and who want to thrive in a fast-paced entrepreneurial environment. We offer a flexible workplace where we count on each other to take initiative. So join the ride – we can’t wait to see where it takes you.
We are looking for a Senior Data Scientist to join the Data Science Marketplace team, whose mission is to improve the efficiency of the carpool marketplace. We aim to ensure that the passengers find a car that suits their needs and the driver has passengers to share their travel cost.
One of BlaBlaCar’s key strategic goals is to leverage data through Machine Learning in order to trigger growth opportunities. Data science has been instrumental to successfully launching new services for the past two years, yet the potential for improvements is still massive. If you are looking for exciting challenges, impacting millions of users and working on a state-of-the-art cloud platform, come and join us!
Among many projects, the team delivers Machine Learning driven tools to optimize the prices of the trips on the platform. BlaBlaCar data stack is composed of the Google Cloud Platform suite (BigQuery, Pub/Sub, GKE, Cloud Run, ...).
Data scientists collaborate closely with ML engineers, data analysts and product managers to deliver end-to-end ML projects from data exploration to production.
- Leveraging machine learning algorithms to optimize the matching between a passenger and a driver
- Contributing to pioneering research on experimentation on data science driven mobility
- Working with software engineers to implement real-time solutions
- Collaborating with data analysts to explore vast amounts of data
As an experienced member in a team of 6 Data Scientists:
- Challenging our data scientist toolbox, and proposing solutions to improve our data science tooling
- Leading large-scale projects
- Mentoring other data scientists
- At least 5 years of professional experience in data or software engineering, including 3 year as a data scientist building predictive machine learning models in production
- Strong knowledge of Machine Learning theory, statistics and probabilities, a PhD is a plus
- Experience in carrying ML based projects, from exploration to deploying models in production and monitoring them
- Fluent in SQL, Python, and good knowledge of main machine learning packages: scikit, xgboost, keras...
- Pragmatic approach to problem: you can design intermediate solutions in an agile environment
- You have excellent communication skills (able to explain your models clearly to both analysts and decision makers), you’re humble, and you enjoy sharing & learning from others
- You embrace change, are able to take a step back, prioritize, and focus on added-value tasks
- You have a results-driven and impact-oriented mindset
- Fluency in English
- If you don’t meet 100% of the qualifications outlined above, tell us why you’d still be a great fit for this role in your application!
What we offer
- An international environment: over 35 nationalities across 7 countries: France, Germany, Spain, Ukraine, Russia, Brazil, Poland.
- Opportunities to learn: 360 onboarding weeks, weekly team-all BlaBlaTalks to learn about what other teams are up to, International Weeks to mingle with other offices, regular Q&A sessions with our leadership, honest discussions about our company KPIs, ‘Fail, Learn, Succeed’ moments where we destigmatize and share moments of failure with others.
- Innovation: Coding Nights to pitch ideas outside our roadmap and make development dreams come true, weekly Product & Tech Demos and blogs to share engineering stories, access to top conferences across Europe.
- Impact: building a product that has a real impact on society and the environment, and sharing an office culture that prioritizes low-waste and eco-friendly practices.
- People-first: wind down from work at our BlaBlaShows, BlaBlaBreak retreats, weekly breakfasts and afterworks (when the sanitary conditions permit it) or meme battles on Slack.
- Shared company principles that guide us in our everyday decision-making and bring us closer to our goal. Find out more about our BlaBlaPrinciples.
Your future benefits
- Tailor-made remote policy: from 2 days per week to full remote (to be discussed with the Talent Acquisition Manager during the first call).
- A fair & competitive salary package
- Employee Stock Ownership plan
- Holidays: 10 days off in addition to the legal 25 days
- Relocation package and visa sponsorship to welcome you wherever you are currently based
- Parental policy (we are a signatory of the #ParentalAct)
- Trainings & career development programs
- Free carpooling & bus-rides wherever whenever
Here’s what your hiring journey will look like.
- A 30-min video-call with one of our Talent Acquisition Managers to get to know you, understand your career expectations, and answer your questions
- An at-home data science test
- A 60-min video-call with Tech Team
- A 60-min video-call with an Engineering Manager
- A 45-min video-call with our VP
- Usually, our hiring process lasts on average 20-25 days, and offers usually come within 48 hours.
- BlaBlaCar is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.