{"id":3165220,"url":"https://alion.io/job/tvarit-senior-data-scientist-metals-division","title":"Senior Data Scientist – Metals Division","company":{"id":2099587,"name":"Tvarit","domain":"tvarit.com","url":"https://alion.io/company/tvarit","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CNN","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Matplotlib","optional":false},{"name":"MLFlow","optional":false},{"name":"NumPy","optional":false},{"name":"Plotly","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"Seaborn","optional":false},{"name":"SQL","optional":false},{"name":"Statsmodels","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-10-23T13:02:18Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-24T21:30:32Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"About TVARIT\nTVARIT GmbH specializes in developing and delivering cutting-edge artificial intelligence (AI) solutions for the metal industry, including steel, aluminum, copper, cast iron, and more. Our software products empower customers to make intelligent, data-driven decisions, driving\nadvancements in Predictive Quality (PsQ), Predictive Maintenance (PdM), and Energy Consumption Reduction (PsE).\nWith a strong portfolio of renowned reference customers, state-of-the-art technology, a talented research team from prestigious universities, and recognition through esteemed awards such as the EU Horizon 2020 AI Prize, TVARIT is recognized as one of the most\ninnovative AI companies in Germany and Europe.\nWe are looking for a self-motivated person with a positive “can-do” attitude and excellent oral and written communication skills in English.\nAbout the Role\nWe are looking for a Senior Data Scientist to join TVARIT’s Metals Division and build AI solutions that improve quality, yield, equipment reliability, and energy efficiency in metal manufacturing plants. The role suits a hands-on data scientist who has worked with real plant data, is fluent in time-series analysis and its tooling, and brings expertise in at least one metal manufacturing domain.\nYou will work directly with process data from customer plants, collaborate with process engineers and plant teams to understand the physics behind the data, and take models from exploration through validation and deployment. You will also contribute to reusable models and components that scale across plants and customers.\nKey Responsibilities\nData Science & Modeling\nDevelop, validate, and improve ML and statistical models for Predictive Quality, Predictive Maintenance, and energy optimization use cases in metal production and processing.\nAnalyze high-frequency and batch time-series data from sensors, PLC/SCADA systems, historians, MES, and quality labs, including alignment of process signals with quality and batch or coil/heat records.\nEngineer features that capture process behavior, such as lags, rolling statistics, frequency-domain features, process-stage segmentation, and physically meaningful derived variables.\nApply anomaly detection, forecasting, classification, regression, and root-cause analysis techniques, and combine data-driven models with process knowledge and physics-based constraints where appropriate.\nDesign robust validation strategies for time-dependent data (e.g., time-based splits, leakage checks, drift analysis) and quantify model uncertainty and limitations clearly.\nPlant & Customer Collaboration\nWork with plant process, quality, and maintenance engineers to understand process flows, operating conditions, and failure modes, and translate them into well-defined modeling problems.\nAssess data readiness and quality at customer sites, identify gaps in instrumentation or data capture, and recommend practical remedies.\nPresent findings, model results, and recommendations clearly to plant teams and customer stakeholders, linking insights to measurable outcomes such as scrap, yield, downtime, and energy consumption.\nDelivery & Productization\nContribute to end-to-end project delivery, from data onboarding and exploratory analysis through model deployment, monitoring, and retraining, working closely with software and data engineering teams.\nWrite clean, tested, reusable Python code and contribute to shared libraries, pipelines, and model templates that can be configured across plants and customers.\nMentor junior data scientists, review their analyses and modeling approaches, and help establish best practices for industrial data science within the division.\nQualifications\nBachelor’s or master’s degree in Data Science, Computer Science, Statistics, Mathematics, Metallurgical, Mechanical, Electrical, or Chemical Engineering, or a related discipline; a PhD is an advantage.\n7+ years of overall professional experience, with substantial hands-on experience in data science or machine learning applied to industrial or manufacturing data.\nMandatory manufacturing plant experience: has worked on data science projects with manufacturing plants, using real process, sensor, and quality data, and working directly with plant operations or engineering teams.\nMandatory time-series expertise: strong practical experience in time-series data analysis, including preprocessing, resampling, alignment, feature engineering, anomaly detection, forecasting, and validation of time-dependent models.\n\nMetal manufacturing domain expertise: in-depth knowledge of at least one metal manufacturing domain, such as steelmaking (BOF/EAF, secondary metallurgy, continuous casting), aluminum (casting, rolling, extrusion), copper, foundry/die casting, forging, rolling, or heat treatment, including the key process variables and quality drivers.\nProven track record of taking models beyond notebooks to validated, deployed solutions that delivered measurable business impact.\nStrong written and verbal communication skills, with the ability to explain technical results to both engineers and non-technical stakeholders.\nWillingness to travel to customer plants as required.\nTechnical Skills\nProgramming: Strong Python skills for data science, with working proficiency in SQL; good coding practices including version control (Git), modular code, and testing.\nData analysis libraries: pandas, NumPy, SciPy, and visualization libraries such as Matplotlib, Plotly, or Seaborn.\nTime-series and ML packages: Hands-on experience with packages such as statsmodels, scikit-learn, sktime, tsfresh, Darts, or Prophet, and gradient boosting libraries such as XGBoost or LightGBM.\nDeep learning: Experience with PyTorch or TensorFlow for sequence models (e.g., LSTM, temporal CNN, transformer-based models) is an advantage.\nStatistics and modeling: Solid foundation in statistics, signal processing, regression, classification, anomaly detection, forecasting, and design of\nexperiments; familiarity with hybrid physics-informed modeling is an advantage.\nI ndustrial data: Familiarity with PLC/SCADA data, historians (e.g., OSIsoft PI or similar), MES, and lab/quality systems, including data quality and contextualization challenges.\nDeployment and MLOps: Understanding of model deployment, monitoring, drift detection, and retraining; experience with tools such as MLflow, Docker, and cloud platforms (Azure preferred; AWS or GCP also relevant) is an advantage","description_format":"text","description_chars":6442,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"All levels","optional":false}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Manufacturing","Industrial AI"],"lifecycle":[{"event":"open","at":"2026-10-23T17:11:10Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":30,"reasons":["conf:0","win:early"],"computed_at":"2026-10-24T06:02:35Z"},"pay":null,"html_url":"https://alion.io/job/tvarit-senior-data-scientist-metals-division","json_url":"https://alion.io/job/tvarit-senior-data-scientist-metals-division.json","meta":{"generated_at":"2026-10-24T22:51:22Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":33508,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-25T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2099587},"rest":"https://alion.io/mcp/rest/get_company?id=2099587"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ftvarit-senior-data-scientist-metals-division"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Ftvarit-senior-data-scientist-metals-division"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Ftvarit-senior-data-scientist-metals-division"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/tvarit-senior-data-scientist-metals-division\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ftvarit-senior-data-scientist-metals-division"}]}