10 annunci per il ruolo di full stack - roma, laz (recruiting in corso)

Data Science Lead
Neurons Lab —Roma, Lazio
- Tempo pieno
- Remoto
- SQL
- AWS
- Python
Solution Architect
Exelab S.r.l. —Roma, Lazio
- Tempo pieno
- Remoto
- Azure
- Inglese
- AWS
Candidatura rapida
Data Engineer
Neurons Lab —Roma, Lazio
- Tempo pieno
- Remoto
- Inglese
- SQL
- AWS
Senior Software Solution Architect
Frontiere —Roma, Lazio
- 40.000 € - 50.000 € all'anno
- Gestionale
- Informatica
- Ingegneria
Senior Back-end Java Developer
NTT Data —Roma, Lazio
- DevOps
- Inglese
- Java
- Lavoro da casa
Senior Software Engineer / Tech Lead – AI-Enabled Development Factory
DGS —Roma, Lazio
- DevOps
- Java
- SQL
Candidatura rapida
Data Scientist Senior - GenAI Solutions
NTT Data —Roma, Lazio
- Java
- SQL
- C++
NETWORK ENGINEER – Packet Core Network
Telebit —Roma, Lazio
- Dal lunedì al venerdì
- Laurea triennale
Candidatura rapida
SOFTWARE ENGINEER
Laser Romae —Roma, Lazio
- Lavoro da casa
Senior Sales Engineer - Italy
Datadog —Roma, Lazio
- Azure
- Vendite
- AWS
Candidatura rapida

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Data Science Lead

Neurons Lab
Roma, Lazio

Candidati adesso

Dettagli della posizione

Tempo pieno

Qualifiche

Data lake
Esperienza IT audit
SQL
AWS
Data science
Python

Descrizione completa della posizione

About the project (description, duration, stage)

Hands-on Data Science Lead on a new engagement with a regulated UK & Ireland credit and lending company. The client has consolidated data from multiple business entities into a newly centralized, anonymized data lake and wants to turn it into validated risk analytics — delinquency, probability of default, credit-policy insight — plus an executive-facing natural-language insight layer.

This is a foundational data-science build, not an agentic-AI project. The early work is unglamorous and hands-on: validating data nobody can yet vouch for, then building defensible models on top. You are the senior data scientist the client is missing — you do the work and own the methodology, while leading a small pod and acting as the human-in-the-loop the client explicitly asked for.

Stage: pre-contract / scoping (Phase 1 = current-state assessment + data validation). Duration: multi-phase, multi-quarter ambition with strong extension probability.

Reporting: Engagement lead / CTO (@Alex Honchar); leads the pod's Data Engineer(s) and the client's offshore data team.

Full-time engagement is preferable.

What you'll actually do (example tasks)

Profile the anonymized lake hands-on — interrogate tens-of-millions-of-row tables and reproduce and validate the team's existing descriptive statistics, so every number is traceable to source (the client cannot currently answer “how do you know that's correct?”).
Build and validate the core risk models yourself: PD, delinquency / roll-rate, early-warning, segmentation and scorecards (WOE / IV, logistic regression, gradient boosting).
Stand up the model-validation discipline that makes outputs audit-defensible: train / test / out-of-time splits, Gini / AUC / KS, calibration, stability (PSI), backtesting and full model documentation.
Define feature logic with the Data Engineer and write it yourself in SQL / dbt / Python; specify the harmonized definitions the semantic layer must serve.
Prototype and validate the natural-language insight layer (text-to-SQL / RAG over the semantic layer); check answer correctness and add guardrails.
Run a credit-policy / cut-off analysis showing where the client could tighten policy or reduce delinquency — the concrete insight their own clients keep asking for.
Lead a small pod (Data Engineer, client's junior offshore data people): set tasks, review work, be the quality bar and the human-in-the-loop.
Front the client's data leadership: present findings, explain methodology to non-technical executives, and shape the phased roadmap / SoW.

Skills (hands-on first)

Expert Python for data science (pandas / Polars, scikit-learn, statsmodels) and strong SQL over large tables
Credit-risk / financial modeling: scorecards, PD, delinquency, segmentation, model validation and governance
Data validation, profiling and feature engineering on messy enterprise data
dbt / semantic modeling; partnering with data engineering on the harmonization layer
GenAI insight layer: text-to-SQL, RAG over structured data, evaluation and guardrails
Methodology, lineage and documentation that survives audit; able to explain it to executives
Leadership of small delivery pods and distributed / offshore teams

Knowledge

GDPR fundamentals (anonymization vs pseudonymization, UK / EU data residency)
AWS analytics stack and Well-Architected (Analytics, Security) for BFSI
UK / EU credit & lending regulatory context (FCA, model governance, fair-lending / explainability) — strong plus
Familiarity with credit-bureau / scoring data products — strong plus

Experience

Key characteristics (ideally 4/4):

Hands-on data science at enterprise scale
Worked with financial-services / credit clients or in-house at a credit / lending company
Cloud hyperscaler experience (AWS preferred)
Technology consulting / client-facing delivery background

Role-specific characteristics:

7+ years hands-on data science, with real credit-risk / financial modeling
Experience building and validating models in a regulated, audited context
Led small data-science teams while still coding personally
Demonstrably comfortable doing the data-cleaning grunt work themselves, not just directing it

Candidati adesso

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full stack offerte - roma, laz

Data Science Lead

Solution Architect

Data Engineer

Senior Software Solution Architect

Senior Back-end Java Developer

Senior Software Engineer / Tech Lead – AI-Enabled Development Factory

Data Scientist Senior - GenAI Solutions

NETWORK ENGINEER – Packet Core Network

SOFTWARE ENGINEER

Senior Sales Engineer - Italy

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