{"id":4192005,"url":"https://alion.io/job/arhs-group-senior-data-scientist-aiml-mlops","title":"Senior Data Scientist – AI/ML & MLOps","company":{"id":54633,"name":"Arhs Group","domain":"arhs-group.com","url":"https://alion.io/company/ares-group","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":75,"open_postings":6,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":34,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenShift","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-11-12T14:24:31Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T19:35:40Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Arηs Group, Part of Accenture, specializes in the management of complex public sector IT projects, including systems integration, informatics and analytics, solution implementation and program management. Our team helps lead clients through digital and information systems design, bringing expertise in a variety of areas ranging from software development, data science and security management to machine learning, cloud, and mobile development. Arηs Group was acquired by Accenture in July 2024.\n For one of our clients, a major European Union institution, we are looking for an experienced Data Scientist to contribute to the design, development and deployment of advanced Data Science, Machine Learning and AI solutions, someone that:\nDesigns and develops the data analytic, data visualisation and artificial intelligence solutions\nElicits and analyses business needs, requirements, and user stories in context of data science processes\nSupports business experts and product owners during definition of requirements and models development\nProduces data scientist related documentation for models and analytical modules\nDefine, collects, and interprets model’s metrics\nManages data science processes artefacts, models repositories and pipelines\nContributes to requirements analysis, testing, quality assurance, change management, models deployment\nTests models and solutions for performance, accuracy, or other business related KPI's\nDesign, plan and manage prototypes\nManaging Data Scientists team\n At least 8 years of total IT experience\nAt least 5 years of experience in a specific position\nAt least 2 certifications among:Certified Analytics Professional (CAP)\nCDP Data Engineer\nCDP Data Analyst\nMicrosoft Certified Azure Data Scientist Associate\n\nKnowledge and skills\nExpert in Advanced Analytics, AI, Machine Learning, and data platforms architecting\nExpert in data analytics and visualisation\nGood understanding modern databases engines such as NoSQL, SQL, Document databases and similar.\nSound knowledge about Python, R or SQL.\nPractical knowledge of Data science, ML models developing and MLOps culture\nPractical experience in building and train Machine Learning and Deep Learning models\nSpecific requirements\nPractical experience in the design and implementation of end-to-end Data Science and Machine Learning solutions, including problem framing, data preparation, feature engineering, model development, validation, deployment, monitoring and iterative improvement.\nPractical experience with Azure-based Data Science and AI services, such as Azure Machine Learning, Azure AI Foundry, Azure OpenAI or comparable AI/ML platforms, including experimentation, evaluation, deployment and lifecycle management.\nPractical experience with MLOps and DevOps practices for Data Science, including version control, reproducible pipelines, model and artefact registries, CI/CD, automated testing, monitoring, documentation and controlled promotion between environments.\nAdvanced practical experience with Python-based Data Science and Machine Learning development, using relevant libraries and frameworks such as pandas, scikit-learn, PyTorch, TensorFlow, MLflow, Spark/PySpark or equivalent technologies.\nPractical experience across multiple Data Science and AI use-case families, such as forecasting and predictive analytics, classification, clustering, anomaly detection, natural language processing, document intelligence, information extraction, retrieval-augmented generation and/or computer vision/video analytics.\nAbility to design and operate Data Science and AI workloads in secure, controlled or restricted environments, including hybrid cloud, on-premises and isolated/air-gapped environments, using containerised deployment where appropriate (e.g. Docker, Kubernetes/OpenShift), including environments using CPU/GPU compute and with limited or no external connectivity.\nAbility to translate business needs and ambiguous problem statements into measurable Data Science requirements, propose realistic technical approaches, develop prototypes, explain results and limitations to non-technical stakeholders and produce clear technical and model documentation.\nPractical understanding of responsible AI and model risk, including data protection, bias, explainability, traceability, human oversight, model limitations and appropriate governance controls, particularly in regulated 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