{"id":847560,"url":"https://alion.io/job/sedona-lead-data-scientist","title":"Lead Data Scientist","company":{"id":689381,"name":"Sedona Digital","domain":"sedona.digital","url":"https://alion.io/company/sedona-2","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-04T06:34:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["GB","RO"],"hiring_countries_total":2,"salary":null,"salary_estimate":{"min_usd":99000,"max_usd":179000,"period":"year","method":"role_seniority_country_cell","sample_n":38},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Agile","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Interpretability","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"AI Agents","optional":true},{"name":"Azure","optional":true},{"name":"Azure DevOps","optional":true},{"name":"CI/CD","optional":true},{"name":"Claude","optional":true},{"name":"Databricks","optional":true},{"name":"Figma","optional":true},{"name":"Gemini","optional":true},{"name":"Git","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Jira","optional":true},{"name":"Looker","optional":true},{"name":"Miro","optional":true},{"name":"Power BI","optional":true},{"name":"Python","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-08-19T00:00:00Z","employer_posted_date":"2026-08-19","last_verified_at":"2026-11-04T20:39:23Z","board_verified":true,"closed_at":null,"days_open":77,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":77},"description":"Accelerate your development and exposure to high-performance data platforms and cloud infrastructure. Join Sedona Digital, a fast-growing scale-up with the ambition to be recognised as one of the leading technology companies in Romania.\nOur global client base needs builders, engineers who enjoy designing and implementing scalable data platforms, have deep expertise in cloud data technologies, and take pride in delivering reliable, well-governed solutions.\nAt Sedona, we:\nObsess about our customers \nBuild robust, scalable technical solutions \nCreate an open, collaborative culture \nInvest in learning and long-term careers \nWe are seeking a Lead Data Scientist to lead the design and delivery of enterprise-scale AI, machine learning, and advanced analytics solutions. This role combines technical leadership, solution architecture, and hands-on data science expertise to help clients transform business challenges into measurable outcomes through data and AI.\nYou will define best practices across the Data & AI lifecycle, architect modern Data for AI platforms, design governance and metadata frameworks, and lead multidisciplinary teams delivering scalable analytical and AI solutions. Working closely with business stakeholders, architects, engineers, and client leadership, you will translate complex requirements into practical, high-value solutions leveraging cloud-native AI and data technologies.\nResponsibilities\nLeadership of a data science or data & AI team/function \nSetup and own best practices for a wide range of applied data science and data for AI techniques \nAct as a consulting data architect for data science or data for AI framework design and implementation \nDesign and leverage data services to solve enterprise analytical or AI requirements including governance metadata e.g. managed RAG, cataloguing, lineage, trust, weighting, usage, history, obsolescence, observability summarisation for risk, compliance, finops & UX. \nTranslate business problems into analytical solutions, identifying opportunities for predictive modelling, optimisation, and data-driven decision-making \nDesign, develop, and deploy machine learning models using techniques such as classification, regression, clustering, and forecasting \nLeverage LLM analytical capabilities by engineering prompts to securely hosted AI models \nApply statistical methods and experimentation techniques (hypothesis testing, A/B testing) to validate models and insights \nConduct exploratory data analysis (EDA) to quantify data asset value, identify patterns, trends, and key drivers within large datasets \nEngineer features and prepare datasets to improve model performance and robustness \nEvaluate and optimise models using appropriate metrics, cross-validation, and tuning strategies \nEnsure model explainability and interpretability, communicating results clearly to both technical and non-technical stakeholders \nDesign and implement MLOps practices including model versioning, monitoring, and retraining strategies \nCollaborate with data engineers to access, prepare, and scale datasets from cloud platforms \nPresent insights and recommendations through compelling storytelling and data visualisation (MI/BI) \nContribute to the design of analytics and AI solutions, focusing on delivering business value rather than infrastructure \nEngage with stakeholders and clients during discovery, experimentation, and solution design phases \nRequirements\n10 years’ working in data-orientated enterprise technology delivery or architecture including \n5 years’ working as a senior data scientist or engineer delivering DS, ML or Advanced Analytics. \n2 years’ working with GCP data technologies. \nHands-on experience with: \nMachine Learning techniques (regression, classification, clustering, time series, etc.) \nStatistical analysis and modeling with production deployments \nEnd-to-end ML lifecycle (data preparation, modeling, evaluation, deployment, monitoring) \nModel performance tuning and validation techniques \nSQL skills and experience working with large datasets \nAI metadata service design and engineering \nDemonstrable, proven ability to lead data teams from design to iterative program delivery and team management. \nDemonstrable, proven ability to elicit, analyse, and document requirements and processes. \nDemonstrable, proven ability with applied data techniques including identification, pipelining/ETL, curation, chunking, modelling, data quality, cataloguing, lineage, package deployment. \nHands-on experience with Agile methodologies and active participation in Agile ceremonies (e.g., sprint planning, retrospectives, backlog grooming). \nSelf-motivated with the ability to work independently and own activities to lead a small, multidisciplinary team. \nStrong problem-solving skills and attention to detail with the ability to work independently and make pragmatic decisions \nAbility to communicate complex data opportunities, AI and analytical concepts clearly to business stakeholders up to C-level. \nComfortable working in a fast-paced, changing environment. \nBachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field \nPreferred Skills (Nice to Have)\nExperience with Generative AI, RAG, and Agentic AI solutions. \nExperience working within Banking, Financial Services or Insurance is highly preferred. \nKnowledge of AI governance, metadata management, and data cataloguing practices. \nExperience within insurance or other regulated industries. \nExposure to multi-cloud data and AI platforms. \nExperience supporting client-facing workshops, solution design, and pre-sales activities. \nRelevant Data, AI, or Cloud certifications. \nKey Tools & Technologies\nGCP Data & AI Components \nDataflow \nDataproc \nBigQuery & ML \nDataplex & Catalogue \nLooker \nVertex AI Agents & Search \nGemini \nAzure Data & AI Component \nADF \nSynapse & Pipelines \nAzureML \nDatabricks \nPurview \nPower BI \nAzureGPT or Claude \nMiro, Figma \nPython, SQL \nCI/CD with Jira, Azure DevOps, Git 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