{"id":2571896,"url":"https://alion.io/job/atlas-copco-group-senior-data-scientist","title":"Senior Data Scientist","company":{"id":1956337,"name":"Atlas Copco Group","domain":"atlascopcogroup.com","url":"https://alion.io/company/atlascopcogroup-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career sitemap","truth_index":{"grade":"A","score":100,"open_postings":3,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":7,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"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":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CatBoost","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Interpretability","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"SHAP","optional":false},{"name":"SQL","optional":false},{"name":"Statsmodels","optional":false},{"name":"XGBoost","optional":false},{"name":"AI Agents","optional":true},{"name":"Anomaly Detection","optional":true},{"name":"Azure","optional":true},{"name":"Databricks","optional":true},{"name":"Microsoft Fabric","optional":true},{"name":"MLFlow","optional":true},{"name":"Power BI","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-14T23:08:52Z","employer_posted_date":"2026-10-23","last_verified_at":"2026-10-24T11:25:24Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":9},"description":"Senior Data Scientist\nAt Global IT Hub, we are more than a technology organization. We are a community of innovators, problem-solvers, and digital pioneers dedicated to accelerating Atlas Copco Group’s business potential through world-class technology solutions. Join us to shape the future of digital transformation, work on global challenges, and create impact for businesses and customers worldwide.\nCLASSICAL MACHINE LEARNING | JOB DESCRIPTION\n\nRole purpose Lead the design, validation, deployment and lifecycle ownership of production-grade classical machine learning solutions that convert business problems into measurable outcomes. The role combines deep algorithmic expertise, disciplined CRISP-DM execution, MLOps practices and senior technical leadership.\n\nRole at a glance\n\nTeam\n\nAI Team, Global IT Hub\n\nPrimary stakeholders\n\nBusiness product owners, IT, data engineering, analytics and domain teams\n\nExperience\n\n5+ years in data science / machine learning, including 3+ years owning production ML solutions\n\nEducation\n\nBachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering or a related quantitative field\n\nKey responsibilities\nPartner with stakeholders to frame ambiguous business needs as well-defined machine learning problems, including decision context, baselines, constraints, risks and measurable success criteria.\nLead end-to-end delivery using CRISP-DM: business understanding, data understanding, data preparation, modeling, evaluation and deployment, with iteration and traceability across phases.\nDesign, train and compare classical supervised and unsupervised learning models; select the simplest approach that meets performance, interpretability, latency, scalability and cost requirements.\nOwn feature engineering, data leakage prevention, sampling strategy, cross-validation, hyperparameter optimization, threshold selection, calibration and robust error analysis.\nBuild reliable evaluation frameworks using fit-for-purpose metrics, statistical tests, confidence intervals, business-cost functions and champion-challenger comparisons.\nTake models from experimentation to production with engineering teams, covering packaging, APIs or batch scoring, versioning, CI/CD, monitoring, retraining and rollback.\nOwn post-production performance, including drift detection, data-quality controls, incident analysis, model refresh decisions and communication of model limitations.\nApply explainability and responsible AI practices through model cards, reproducibility, bias and fairness checks, audit trails, privacy-aware design and human oversight.\nSet technical standards, conduct design and code reviews, mentor data scientists, create reusable components and influence the classical ML roadmap.\nCommunicate recommendations, uncertainty, assumptions and trade-offs clearly to technical teams, business leaders and governance forums.\nMandatory Technical Skills\nAdvanced Python and SQL, with strong hands-on use of NumPy, Pandas, scikit-learn, SciPy and statsmodels; ability to write modular, tested and maintainable code.\nDeep knowledge of regression and classification: linear and logistic regression, regularization, decision trees, Random Forest, Extra Trees, XGBoost, LightGBM, CatBoost, support vector machines, k-nearest neighbors, Naive Bayes and appropriate ensemble methods.\nStrong understanding of unsupervised learning: k-means and hierarchical clustering, DBSCAN, PCA and other dimensionality-reduction methods, anomaly or outlier detection and segmentation evaluation.\nPractical knowledge of forecasting and statistical modeling, including ARIMA/SARIMA, exponential smoothing, trend and seasonality, lag features, back-testing and prediction intervals.\nExpertise in feature selection and engineering, missing-value treatment, categorical encoding, class imbalance, resampling, pipeline design and prevention of target leakage.\nRigorous evaluation skills across ROC-AUC, PR-AUC, precision, recall, F1, log loss, RMSE, MAE, MAPE/WAPE, lift, gain, calibration and business-impact metrics, selected according to the use case.\nHands-on optimization using grid, random or Bayesian search, with sound cross-validation design for grouped, temporal and imbalanced datasets.\nPractical MLOps experience with experiment tracking, model registry, source control, automated testing, Docker, CI/CD, deployment and model or data monitoring.\nWorking knowledge of explainability techniques such as feature importance, permutation importance, partial dependence and SHAP, including their limitations.\nExperience handling large structured datasets and collaborating on scalable data pipelines in a cloud or distributed environment.\nSenior-level capabilities\nIndependently owns technical direction and delivery for complex ML use cases from discovery through production adoption.\nChallenges weak problem formulations and prevents unnecessary use of ML when rules, analytics or simpler statistical methods are more suitable.\nMakes defensible trade-offs among accuracy, interpretability, maintainability, latency and cost.\nReviews experimental design and model evidence before approving production release.\nMentors team members and raises standards for coding, documentation, reproducibility and stakeholder communication.\nGood-to-have experience\nDomain exposure in manufacturing, industrial operations, supply chain, service, pricing, finance, sales or customer analytics.\nOptimization, causal inference, survival analysis, uplift modeling, recommendation systems, geospatial analytics or graph-based techniques.\nAzure Machine Learning, Databricks, Microsoft Fabric, MLflow, Spark and enterprise data sources such as SAP.\nExperience integrating classical ML models into applications, APIs, decision-support workflows or Power BI solutions.\nAwareness of deep learning and generative AI, with the judgment to benchmark them against classical ML rather than defaulting to higher-complexity approaches.\nCore algorithm and method coverage\nCandidates are expected to demonstrate depth in several categories and sound model-selection judgment across the full landscape. This is not a requirement to have used every library or algorithm in production.\n\nCapability area\n\nExpected knowledge\n\nSupervised learning\n\nRegression, classification, tree ensembles, boosting, SVM, nearest-neighbor and probabilistic baselines.\n\nUnsupervised learning\n\nClustering, dimensionality reduction, anomaly detection and segmentation validation.\n\nTime-dependent modeling\n\nForecasting, temporal validation, lag and rolling features, seasonality and uncertainty.\n\nData and features\n\nEDA, data-quality assessment, transformations, encoding, imbalance handling and leakage control.\n\nEvaluation\n\nMetric selection, baselines, validation design, statistical significance, calibration and business value.\n\nProduction lifecycle\n\nReproducibility, versioning, deployment, observability, drift, retraining and retirement.\n\nGovernance\n\nExplainability, documentation, bias and fairness assessment, privacy, approvals and auditability.\n\nWhat you can expect from us\nMeaningful global business problems and access to cross-functional expertise.\nFreedom to choose fit-for-purpose methods and challenge unnecessary complexity.\nA collaborative environment focused on engineering quality, responsible AI and measurable value.\nOpportunities to mentor, build reusable capabilities and shape the enterprise AI practice.\nAt Global IT Hub, we believe exceptional talent drives exceptional transformation. As a strategic partner for next-generation digital transformation, we empower our people to innovate boldly, collaborate globally, and turn ideas into solutions that create lasting business value. Join a team where learning never stops, innovation is part of our DNA, and your contribution helps shape the future of a global industry leader.\nRole Evolution - Next 3-4 Years\nEvolve from predictive modelling toward decision intelligence, combining prediction, causal analysis, simulation and optimization.\nBuild hybrid AI solutions integrating classical ML with Generative AI, foundation models and AI agents.\nEstablish systematic benchmarking across classical ML, deep learning and emerging AI models to select the best approach based on performance, explainability, scalability and cost.\nDrive automation of the end-to-end ML lifecycle, including feature engineering, experimentation, deployment, monitoring, drift detection, retraining and model retirement.\nDevelop advanced model evaluation frameworks covering technical performance, business impact, robustness, explainability, fairness and operational reliability.\nAdvance model observability and continuous learning, proactively managing data drift, concept drift and model degradation.\nStrengthen Responsible AI and model governance, including lineage, reproducibility, explainability, bias assessment, human oversight and auditability.\nDevelop reusable ML frameworks, components and engineering standards to accelerate enterprise-wide AI development.","description_format":"text","description_chars":9018,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Industrial Machinery"],"lifecycle":[{"event":"open","at":"2026-10-14T23:08:52Z"}],"visa":[],"liveness":{"score":40,"band":"fade","label":"Fading","p_open":1,"p_active":0.736,"p_room":0.55,"age_days":9,"expected_fill_days":7,"reasons":["conf:3","velocity","win:tail","comp:brand"],"computed_at":"2026-10-24T06:02:35Z"},"pay":null,"html_url":"https://alion.io/job/atlas-copco-group-senior-data-scientist","json_url":"https://alion.io/job/atlas-copco-group-senior-data-scientist.json","meta":{"generated_at":"2026-10-24T13:55:56Z","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":20097,"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":1956337},"rest":"https://alion.io/mcp/rest/get_company?id=1956337"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fatlas-copco-group-senior-data-scientist"},{"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%2Fatlas-copco-group-senior-data-scientist"},{"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%2Fatlas-copco-group-senior-data-scientist"},{"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/atlas-copco-group-senior-data-scientist\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fatlas-copco-group-senior-data-scientist"}]}