{"id":2915870,"url":"https://alion.io/job/variene-ai-data-operations-analyst","title":"AI Data Operations Analyst","company":{"id":4452340,"name":"Variene","domain":"variene.ai","url":"https://alion.io/company/variene","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":null},"role":"Analytics","role_family":"Analytics","seniority":"junior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Athens, Greece"],"countries":["GR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":17500,"max_usd":43000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1679},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"CVAT","optional":false},{"name":"FFmpeg","optional":false},{"name":"Microsoft Excel","optional":false},{"name":"Python","optional":false},{"name":"YOLO","optional":false},{"name":"C","optional":true},{"name":"Label Studio","optional":true},{"name":"Power BI","optional":true},{"name":"Roboflow","optional":true}],"status":"live","first_seen_at":"2026-10-14T11:38:25Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-21T12:36:42Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"About Variene\nVariene develops advanced AI and computer vision software for defense and autonomous systems, designed to operate in demanding real-world environments.\nOur technology brings intelligent perception to platforms operating across ISR, autonomous systems, and other mission-critical defense applications.\nOur models are only as good as the data behind them.\nThe Role\nAs our AI Data Operations Analyst, you will own the full lifecycle of the data that trains our computer vision models: from finding the right video and imagery, to preparing and categorizing it, assigning it to our annotation team, checking the results, and reporting on what the data tells us.\nYou will sit in management and product meetings to understand our current and upcoming projects, and you will translate those priorities into the right material, at the right time, for the annotation team. You will be the bridge between the people who set product direction and the people who create our training data, and you will bring the numbers and the feedback back to product development.\nReports to: Head of Product / CTO. Works closely with: Leadership, ML engineers, DevOps, and the Annotation Team Manager.\nGood to know: due to the nature of our work, some of the material you will review may include footage from real conflict zones, which can occasionally be distressing. We take this seriously and will discuss it openly during the hiring process.\nWhat You'll Do\nTake part in management and product meetings to stay aligned with active and upcoming projects.\nTranslate project goals into concrete data needs: target classes, scenarios, environments, and edge cases.\nAgree with product development on priorities for the annotation team and keep them visible and up to date.\nIndependently search for, evaluate and secure video and imagery (including FPV, aerial, maritime and thermal footage) relevant to each project.\nJudge each source for relevance, quality, volume and suitability for secure, off-cloud use.\nCheck, together with the DPO/Legal function, that we have the right to use each source (licensing, platform terms, personal data) before it enters our pipeline.\nMaintain a rigorous, up-to-date source register.\nEdit and prepare material for annotation: clipping, trimming, format conversion, frame extraction, de-duplication and cleaning.\nCategorize and tag material with consistent metadata (class, scenario, sensor type, conditions, source) so every clip is easy to find and trace.\nPackage targeted batches and assign them to the annotation team in CVAT, with clear instructions on what to look at and what to look for.\nVerify the integrity and completeness of every batch before it reaches the team.\nAct as a second level of quality control: run sample-based reviews of completed annotations, in coordination with the Annotation Team Manager, who owns day-to-day review.\nCheck that annotations are not only correct, but useful for what the model needs (coverage, consistency, edge cases).\nCollect annotator feedback and recurring edge cases and turn them into clearer guidance and better batches.\nTrack and analyze the data coming from our tools (CVAT and internal systems): annotation throughput and turnaround time, number of annotated objects per class, rejection and correction rates, and backlog.\nSpot gaps and imbalances in the datasets (e.g., classes or scenarios that are under-represented) and recommend what to source next.\nProduce clear, regular reports and dashboards for leadership and product development.\nCollect productivity and quality metrics in line with Variene's internal policies and data protection requirements.\nDocument sources, preparation steps and dataset versions so every dataset is traceable and repeatable.\nFollow Variene's security and data-handling procedures for off-cloud work.\nKnowledge and Experience\n2-4 years of experience in AI/computer vision data operations, data annotation (lead, QA or coordination roles), dataset curation, or a similar data-focused role.\nHands-on experience handling and editing video data (clipping, conversion, frame extraction) with tools such as FFmpeg.\nBasic Python scripting for downloading, organizing and processing files and data.\nStrong Excel skills (pivot tables, charts); ability to turn raw numbers into clear reports.\nProven ability to research and source material independently and assess its quality.\nUnderstanding of how annotation works and of common annotation formats (e.g., COCO, YOLO).\nExcellent written and spoken English; Greek is a plus.\nHighly Valued\nExperience or familiarity involving:\nAnnotation platforms such as CVAT, Label Studio or similar, including their analytics and reporting features.\nPower BI or similar dashboarding tools.\nDataset management tools (e.g., Roboflow, FiftyOne).\nDefense, maritime or aerial footage; ISR data workflows; thermal (IR) imagery.\nOpen-source intelligence (OSINT) research methods.\nWorking in on-premise or air-gapped environments.\nEligibility for a national or NATO security clearance.\nProfessional Profile\nWe are looking for someone with:\nInitiative and high ownership, comfortable finding their own way to the data we need.\nExceptional data hygiene habits and an eye for detail.\nStructured, analytical thinking and comfort with numbers.\nClear, diplomatic communication across leadership, engineers and annotators.\nCuriosity about how data quality directly affects model performance.\nDiscretion and a strong sense of responsibility when handling sensitive material.\nWhat Success Looks Like\nSuccess is not measured by data volume alone.\nWithin your first months, you will help Variene:\nkeep the annotation team supplied with relevant, well-prepared material, with no idle time;\nmake sure annotation effort is always aimed at what product development needs most;\nbuild balanced datasets, with clear visibility of coverage per class and scenario;\nreduce rework through better batches, clearer guidance and early quality checks;\ngive leadership a clear, regular picture of data progress, quality and gaps.\nCompetitive compensation based on experience.\nPerformance-related 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