Data Scientist, AI

Volta Software; Lombardia; Ibrido; EUR 70.000 - 110.000; Tempo pieno

Descrizione del lavoro

Volta Software is seeking an Applied Data Scientist to measure and improve the accuracy of its AI system across real purchase orders. You will build evaluation harnesses, perform error analysis, and enhance retrieval and extraction from complex documents.

You will work with Python, Pandas/Polars, and LLM APIs to turn varied data sources (emails, PDFs, spreadsheets) into clean, structured order lines, with rigorous per-field metrics and CI-integrated tests. Hybrid Milan/Paris location preferred.

Competenze

• 3+ years shipping ML or NLP to production.
• Strong Python, pandas or polars and testing habits.
• Hands-on with LLM APIs (Gemini, OpenAI, Anthropic or similar).
• Evaluation datasets and metrics (precision/recall, per-field accuracy, confidence calibration).
• Search and retrieval (Elasticsearch/OpenSearch, BM25, embeddings, reranking).
• Solid SQL.
• Fluent English; French or Italian.
• Nice to have: GCP (Cloud Run, Vertex AI); LLM tracing/evaluation tooling.

Mansioni

• Own the accuracy number across customers, per field, per release.
• Build evaluation harnesses and CI-integrated tests for regression protection.
• Improve product and customer matching using Elasticsearch-based retrieval.
• Turn emails, PDFs, and spreadsheets into clean order lines with prompts and schemas.
• Perform rigorous error analysis to identify root causes and fixes and verify in production.
• Turn customer fixes into reusable templates and configurations by industry.

Conoscenze

Python

Pandas

Polars

LLM APIs

Evaluation metrics

SQL

English fluent

French or Italian

Formazione

MSc in Computer Science/Statistics/Applied Math

Strumenti

Elasticsearch/OpenSearch

SQL databases

LangFuse (tracing)

Descrizione del lavoro

You’ll own how accurate Volta's AI is, and you’ll make it measurably better, customer by customer. You’ll build the evaluation harnesses, run the error analysis and improve extraction and matching on real purchase orders from enterprise customers. You’ll report to the Head of AI: this role is about measurement, retrieval and LLM-based extraction, not about training models.

• Own the accuracy number: we are working towards 90% accuracy across customers. Define how it's measured per field (customer, ship-to, product, quantity, unit of measure, price), track it per customer and per release, and make it go up

• Build evaluation that runs itself: per-customer evaluation harnesses and golden datasets built from real, anonymised orders, wired into CI so a regression is caught before release, not by the customer

• Make matching better: product and customer matching against catalogues of thousands of SKUs and messy master data, with Elasticsearch-based retrieval, synonyms, reranking and confidence scores

• Improve extraction with LLMs: prompts, schemas and pre-processing steps that turn an email body table, a multi-page PDF or a 100-line spreadsheet into clean order lines

• Do error analysis like an engineer: turn a pile of customer issues into categories, root causes and the one fix that closes ten tickets, then verify in production after the release

• Turn customer fixes into configuration: per-customer behaviour lives in prompts and configuration, not in code branches, and recurring patterns become reusable templates by industry and document type

Applied data scientist who measures things: 3+ years shipping ML or NLP to production, judged on a metric that mattered to users LLM applications in production: extraction, classification or retrieval built on LLM APIs with structured outputs and real evaluation, not only demos or notebooks Search and matching: entity resolution, record linkage or product search, ideally with Elasticsearch, OpenSearch or hybrid lexical plus vector retrieval Evaluation discipline: test sets, metrics, label noise and regression tracking over time Real-world documents: messy PDFs, spreadsheet exports, emails in several languages Writes production code: Python that other people can review, test and run in CI Must have: strong Python, pandas or polars and testing habits; hands-on with LLM APIs (Gemini, OpenAI, Anthropic or similar); evaluation datasets and metrics (precision and recall, per-field accuracy, confidence calibration); search and retrieval (Elasticsearch or similar, fuzzy matching, BM25, embeddings, reranking); solid SQL; rigorous error analysis; fluent English; French or Italian (many documents are written in them) Nice to have: LLM tracing and evaluation tooling (Langfuse or similar); document parsing and OCR; B2B commerce data (catalogues, units of measure, price lists, ERP master data); GCP (Cloud Run, Vertex AI) Degree: MSc in Computer Science, Statistics, Applied Math, Computational Linguistics or a related quantitative field, or equivalent practical experience Location: Milan or Paris, hybrid At Volta, we believe diversity makes our teams stronger. We welcome all applications, regardless of gender, background, or experience.