Data Scientist (M/F)

ARCHE consulting; Województwo mazowieckie; On-site; PLN 180,000 - 250,000; Full time

Job summary

Arche Consulting Sp. z o.o. is seeking a senior Data Scientist for a global IT services client.

You will design and develop analytics, AI and ML solutions, translating business needs into technical requirements. You will lead end-to-end ML lifecycle: data prep, training, deployment, monitoring and governance, while promoting reusable data science assets and best practices across teams. The role requires advanced Python skills, experience with Azure AI services, MLOps, and a track record of

Qualifications

• Certifications in analytics or data science are required.

Responsibilities

• Design and develop advanced analytics, data visualisation, AI and ML solutions.
• Collaborate with business stakeholders to identify needs and translate them into technical requirements and AI/ML use cases.
• Develop, test, deploy and continuously improve ML and DL models.
• Manage the end-to-end DS lifecycle, including model performance evaluation, monitoring and optimization.
• Manage model repositories, artefacts, pipelines and deployment processes.
• Establish and promote DS standards, best practices and reusable solutions.
• Prepare technical documentation covering models, analytical solutions and data processes.
• Support testing, QA, change management and solution deployment.
• Estimate development effort and plan analytical projects and prototypes.
• Facilitate Design Thinking workshops and coordinate DS teams across the organisation.

Skills

Python

Pandas

Scikit-learn

PyTorch

TensorFlow

MLOps

AI/ML project delivery

Communication

Education

CAP

CDP Data Engineer

CDP Data Analyst

Microsoft Certified Azure Data Scientist Associate

Tools

Azure Machine Learning

Docker

Kubernetes

Job description

Our client is a global IT services and consulting company specializing in digital transformation, cloud, cybersecurity, and managed services for enterprise clients.

Responsibilities

design and develop advanced analytics, data visualisation, AI and Machine Learning solutions,

collaborate with business stakeholders to identify needs and translate them into technical requirements and AI/ML use cases,

develop, test, deploy and continuously improve Machine Learning and Deep Learning models,

manage the end-to-end Data Science lifecycle, including model performance evaluation, monitoring and optimisation,

manage model repositories, artefacts, pipelines and deployment processes,

establish and promote Data Science standards, best practices and reusable solutions,

prepare technical documentation covering models, analytical solutions and data processes,

support testing, quality assurance, change management and solution deployment,

estimate development effort and plan analytical projects and prototypes,

facilitate Design Thinking workshops and support the development of Data Science capabilities across the organisation, including coordinating Data Science teams.

Requirements

at least two relevant certifications: Certified Analytics Professional (CAP), CDP Data Engineer, CDP Data Analyst, Microsoft Certified Azure Data Scientist Associate or internationally recognised equivalents,

advanced Python skills, including pandas, scikit-learn and PyTorch, TensorFlow or equivalent ML frameworks,

proven experience in designing, developing and deploying at least two advanced AI/ML solutions,

hands‑on experience with the end‑to‑end Machine Learning lifecycle, including data preparation, feature engineering, model training, validation, deployment and monitoring,

practical experience with Azure AI / Data Science services (Azure Machine Learning, Azure AI Foundry, Azure OpenAI) or comparable platforms,

knowledge of MLOps practices, including MLflow, model registries, automated pipelines, CI/CD, versioning and monitoring,

experience across different AI/ML domains, such as predictive analytics, forecasting, anomaly detection, NLP, RAG or computer vision,

experience working with large‑scale structured and unstructured datasets, SQL/NoSQL databases and data engineering processes,

understanding of responsible AI, model governance, explainability and data protection,

strong analytical, problem‑solving and communication skills, with the ability to translate business needs into technical solutions and document results.

Nice to have:

experience with on‑premises, hybrid cloud or air‑gapped environments,

knowledge of Docker, Kubernetes/OpenShift and GPU computing,

experience in regulated industries, public administration or banking,

background in an AI Centre of Excellence, developing standards and reusable solutions, or leading Data Science teams.