Data Scientist & Analytics Engineer
Ledger is hiring a Data Scientist & Analytics Engineer in Paris, France. Level rates it ; you can apply on Ledger.
Level's read on this job
- On Level's scale, this data scientist role is Uses AI ●●○○: an ordinary role that requires AI tools.
- Some of the work can be done remotely, according to the posting.
- The posting mentions dbt, Snowflake and Tableau among its tools and skills.
- Level classes it as a mid-level role.
- Ledger has 11 other open roles on Level.
AI in this role
Own consumer sales analytics end-to-end and work as part of an AI-augmented team utilizing LLM-based tools for data workflows.
We’re a team of experts pushing the limits of what’s possible, united by our common goal to unlock true freedom through digital ownership, making technology accessible for all. We believe in a world where users, creators and enterprises manage their value with ownership and freedom. Our curiosity drives us to innovate, empowering individuals on a global scale. We believe change is constant and our team moves forward as one, with a culture of problem-solving where every employee is empowered and supported to challenge tradition and create solutions. Our mission is simple: to make self-custody accessible and give people the keys to their own financial futures. If you want to make a true impact, we want you to join us at Ledger.
At Ledger, we’re proud to be the global platform for digital assets and Web3, with over 20% of the world’s crypto assets secured through our Ledger devices. With our headquarters in Paris, and offices in Vierzon, Grenoble, Montpellier, London, Portland, Geneva, Zurich and Central Singapore, we have a team of around 600 professionals developing a variety of products and services to enable individuals and companies to securely buy, store, swap, grow and manage crypto assets – including the Ledger hardware wallets line with more than 7.5 millions units already sold in 200 countries.
The team:
You'll join the Data Team, responsible for turning data into actionable insights and recommendations for our consumer sales and marketing operations across all channels.
What you’ll be doing:
Own consumer sales analytics end to end: Ledger.com, Amazon, wholesale and resellers, from sell-in to sell-out, with one consistent view of revenue, units and margin
Measure the true incremental impact of pricing, promotions and marketing campaigns with rigorous causal methods and experiments, and turn the results into budget recommendations for the Growth team
Develop and improve data models (dbt/Snowflake) across varied data sources (sales, sell-out, marketing, web analytics) with the support of the data engineering team; co-own data quality alerting
Own performance marketing measurement (attribution, ROAS, CAC), reconciling ad platforms, GA4 and internal sales data into a single source of truth
Build and maintain Tableau dashboards that give business teams a clear, reliable view of their performance
Model customer value (LTV, repurchase behavior, services attach) and feed it back into marketing ROI decisions
Work as part of an AI-augmented team: use LLM-based workflows and AI agents daily to automate analyses and accelerate delivery
Create and deliver presentations that turn complex analyses into clear recommendations for stakeholders up to ExCom level
What we’re looking for:
Proven hands-on data science experience applied to business decisions: you have run causal inference studies (CausalImpact, difference-in-differences, synthetic control, geo-experiments) and/or built predictive models (LTV, repurchase, propensity) whose results changed a budget or pricing decision
Strong statistical rigor: comfortable with experiment design, confidence intervals and power, and able to clearly say when a result shows no detectable effect
2 to 4 years of experience in data analytics, analytics engineering or data science
Strong SQL
dbt skills are a plus
Tableau skills are a strong plus
Data driven and business oriented; able to challenge stakeholders' methodology
Familiarity with marketing/web analytics data: GA4, ad platforms (Google Ads, Amazon Ads), affiliation networks are a strong plus
At Ledger, we are dedicated to continually investing in our employees which is why we offer more than just salaries; we provide comprehensive compensation packages that include a wide range of benefits. Here are some of the benefits you can look forward to:
Flexible work options - Our hybrid policy allows employees to work from home up to 3 times per week
Health & Wellness support - Health and Life Insurance.
Financial growth opportunities - Employees can become shareholders in Ledger as well as other financial benefits depending on your country of work.
Commuter allowance - Ledger offers a commuter allowance to contribute to your preferred means of transportation.
Learning & Development - A comprehensive suite of training solutions providing a personalised learning experience for every employee.
For regionally specific benefits, your Talent Acquisition contact will be able to provide you with more information.
We’re committed to building an inclusive hiring process. If you need any adjustments or accommodations, just let us know, we’ll do our best to support you.
How we rate this
Data Scientist & Analytics Engineer at Ledger rates 45 out of 100 for how much of the daily work is AI. That makes it Uses AI (AI Level 2 of 4). The level is about AI in the job, not seniority.
Uses AI. An ordinary role that requires AI tools.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data scientist jobs at this level
Pay
- Typical data scientist job
- $167k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where data science was part of your work. What did you do?
- Tell me about a project where analytics engineering was part of your work. What did you do?
- Tell me about a project where pricing analytics was part of your work. What did you do?
- Tell me about a project where marketing attribution was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI agents, Data Science, Analytics Engineering, Pricing Analytics, and Marketing Attribution. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Put the AI tool in a bullet point about what you did, not just in a skills list. This role treats it as a required part of the job.
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