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UpClear

Revenue management software for consumer brands

Lead Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeParis, FranceNo salary listed

About the Job

Requirements

  • At least 6 years of data science or machine learning experience, with production models used daily by business teams or customers.
  • A Bac+5 qualification or doctorate in statistics, applied mathematics, econometrics, computer science, or a closely related field.
  • Strong command of regression, time series, and causal reasoning, including the ability to explain baseline, uplift, price elasticity, and model certainty.
  • Experience with Bayesian and hierarchical models using PyMC, Stan, or equivalent, or the ability to learn these tools quickly.
  • Production-quality Python, including pandas or Polars, scikit-learn, statsmodels, testing, packaging, and code review.
  • Experience automating production model training and scoring pipelines, versioning models, and monitoring their behavior after deployment, ideally on Azure.
  • Experience building production LLM features beyond demos, including prompting, tool calling, RAG, and evaluation.
  • SQL and Spark experience for large-scale data.
  • Experience mentoring or supporting data scientists and prioritizing a roadmap based on business value.
  • Ability to explain models to commercial audiences and challenge requests unsupported by the data.
  • Fluent French and English; English is the working language with clients.

Responsibilities

  • Own UpClear's data science models, from raw retail data to the figures customers use to make decisions.
  • Take ownership of and improve RGM Labs models for sales decomposition, baseline forecasting, promotional uplift, price elasticity, and related what-if simulations.
  • Industrialize the models by automating training and refreshes for each customer, implementing back-testing and monitoring, and making results reproducible and explainable.
  • Work with retail panel data from Circana and Nielsen and customer sales data on Microsoft Fabric, including LLM-assisted matching of retailer products to customer SKUs.
  • Integrate AI into products by designing LLM-based features and assistants using Azure OpenAI, tool calling, and RAG, with safeguards ensuring figures come from the models.
  • Define the data science roadmap with product and company leadership, select and scope projects, and demonstrate their value to customers.
  • Write production Python, review the team's code and models, and maintain high standards.
  • Explain model results to Revenue Management and commercial teams, and support the sales team during demos and pilots.
  • Recruit and define team profiles, conduct interviews, and make initial hires, starting with a data scientist.
  • Develop junior team members' skills in modeling, code quality, and customer relations.
  • Establish team practices for code review, experiment tracking, model validation, documentation, and handoffs.
  • Grow the team in line with AI and data science projects, justifying each additional hire.
  • Work within the Data team and collaborate with product, development, and customer-facing teams.

Desired Qualifications

  • Experience in FMCG and retail, including Revenue Growth Management, promotional effectiveness, pricing, or retail panel data such as Circana, Nielsen, or retailer point-of-sale data.
  • Experience with marketing mix modeling, particularly Bayesian methods.
  • Optimization experience with linear or integer programming, for example recommending promotional plans under budget constraints.
  • Experience with Microsoft Fabric, Databricks, Azure Machine Learning, or MLflow.
  • Experience operating the same model for dozens of SaaS customers, each with their own data.
  • Document AI experience extracting data from invoices, claims, or other business documents.
  • Enough product engineering experience with FastAPI or web technologies to expose a model through an API.

About the Company

UpClear

Revenue management software for consumer brands

UpClear provides a Blue Revenue Management platform that helps consumer goods brands maximize revenue performance and trade investment returns. It offers a centralized, collaborative software system that serves end-to-end processes—from Annual Operating Planning to Account Planning and Execution—acting as a single source of truth, with automated tasks and actionable insights. The platform is used by brands globally and supported by teams in New York, London, Paris, and Singapore. What makes UpClear different is its focus on end-to-end revenue management for consumer goods, combining a central data repository, automated workflows, and cross-market collaboration to turn data into executable plans. The company’s goal is to help brands improve revenue results and optimize trade spend across their portfolios.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
New York City, New York
Founded
2007

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