{"id":2526962,"url":"https://alion.io/job/tractian-data-scientist-predictive-maintenance","title":"Data Scientist - Predictive Maintenance","company":{"id":48244,"name":"TRACTIAN","domain":"tractian.com","url":"https://alion.io/company/tractian","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Gem","truth_index":{"grade":"C","score":63,"open_postings":69,"ghost_share":0.623,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-15T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["BR"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Time Series Forecasting","optional":false}],"status":"live","first_seen_at":"2026-06-22T16:32:27Z","employer_posted_date":"2026-09-23","last_verified_at":"2026-10-15T14:44:41Z","board_verified":true,"closed_at":null,"days_open":114,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":114},"description":"Data Science at Tractian\nThe Data Science team at Tractian focuses on extracting valuable insights from vast amounts of industrial data. Using advanced statistical methods, algorithms, and data visualization techniques, this team transforms raw data into actionable intelligence that drives decision-making across engineering, product development, and operational strategies. The team constantly works on optimizing prediction models, identifying trends, and providing data-driven solutions that directly enhance the company’s operational efficiency and the quality of its products.\nWhat you'll do\nAs a Data Scientist - Predictive Maintenance at Tractian, you will work at the intersection of advanced data science and industrial operations. Your mission is to develop cutting-edge algorithms and predictive models to monitor and predict equipment failures before they occur, optimizing asset reliability and reducing downtime. You’ll face complex challenges involving large-scale time-series data, real-time data processing, and machine learning applications, while collaborating closely with engineers and laboratory teams to ensure our predictive maintenance solutions remain industry-leading.\nResponsibilities\nDevelop predictive maintenance algorithms using machine learning techniques for time-series data.\nAnalyze sensor data streams to identify patterns that predict equipment failure.\nResearch and stay up to date with academic literature and state-of-the-art condition monitoring techniques, translating relevant advances into practical solutions.\nCollaborate with engineers to improve data pipelines and enhance model accuracy.\nBuild scalable, real-time models for low-latency predictions.\nCreate diagnostic tools that enable data-driven maintenance decisions.\nWork with the laboratory team to design experiments and develop failure datasets using real machinery to validate hypotheses, develop new models, and optimize existing ones.\nContinuously refine models based on real-world performance, experimental results, and feedback.\nRequirements\nExpertise in machine learning, time-series analysis, and anomaly detection.\nProficiency in Python and common data science and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch).\nSolid understanding of signal processing concepts and hands-on experience with industrial sensor data (e.g., vibration, current, temperature, pressure).\nAbility to read, interpret, and apply insights from academic literature and state-of-the-art research in condition monitoring and fault diagnosis.\nExperience designing experiments to validate hypotheses and benchmark models.\nStrong problem-solving skills and ability to handle noisy, high-dimensional data.\nAdvanced English and Portuguese.\nBonus Points\nFamiliarity with both academic research and real-world applications in condition monitoring, fault diagnosis, and prognostics (e.g., vibration-based methods, model-based vs. data-driven approaches).\nExperience translating academic methods into robust, production-ready algorithms.\nPrior experience working in industrial or manufacturing environments.\nCompensation & Benefits\nCompetitive Compensation\n30 days of paid annual leave\nEducation and courses stipend\nEarn a trip anywhere in the world every 4 years\nR$1.035/month for meals allowance\nHealth plan with national coverage and without coparticipation\nDental Insurance: we help you with dental treatment for a better quality of life.\nWellhub Membership: Access a wide range of gyms and training programs.","description_format":"text","description_chars":3501,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"Portuguese","level":"Advanced (C1)","optional":false},{"language":"English","level":"Advanced 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