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ML Engineer — forecasting & FP&A

AI role · Open offer

ML Engineer — forecasting & FP&A

Pigment

Permanent (CDI)
Full-time
Senior
Data science
Hybrid
9 Rue des Colonnes, Paris, IDF 75002, France · Hybrid
Paris, France
EUR 78k-105k/year + BSPCE

Employer

Pigment

Marketplace

Posted on Ganloss

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At a glance

How you'll work, where, and key listing dates.

Employment: Permanent (CDI), Full-time
Workplace: Hybrid
Location: 9 Rue des Colonnes, Paris, IDF 75002, France · Hybrid
Compensation: EUR 78k-105k/year + BSPCE
Experience level: Senior
Team: Data science
Benefits & perks: BSPCE, produit hypergrowth.
How to apply: Séries temporelles ou forecasting en prod.
Company site: www.pigment.com
Listed: Jul 25, 2026
Updated: Aug 3, 2026

About this role

What the team is looking for and how this position fits.

Pigment is hiring a Senior ML Engineer — forecasting & FP&A on the Data science team (Hybrid · Paris, France). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Pigment est une plateforme FP&A moderne avec forecasting et scénarios assistés par ML.

ML Engineer (Paris) : séries temporelles, modèles de planification, et explicabilité pour finance.

What you will do

Deliver production-grade forecasting ML with measurable quality, latency, and cost targets.
Build offline evals and online monitoring before scaling to full traffic.
Collaborate with product, design, legal, or domain experts on safe rollout.
Write clear RFCs, runbooks, and postmortems for cross-team learning.

What we look for

Multiple shipped ML/LLM features—not coursework-only portfolios.
Strong Python, SQL, and experimentation discipline.
Clear communication for remote or hybrid stakeholders.
Work authorization for the listed country.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
SQL
Analytical SQL for features, cohorts, label quality, and experiment analysis at scale.

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

dbtTool
Regular use of dbt in the LLM & generative AI stack—configuration, debugging, and team conventions.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Séries temporelles ou forecasting en prod.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, and dbt.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Paris, France)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: EUR 78k-105k/year + BSPCE, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

Séries temporelles ou forecasting en prod.

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ML Engineer — forecasting & FP&A

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