{"id":2023245,"url":"https://alion.io/job/scorp-io-machine-learning-engineer-data-scientist-fh","title":"Machine Learning Engineer / Data Scientist (F/H)","company":{"id":2594280,"name":"SCorp-io","domain":"scorp-io.com","url":"https://alion.io/company/scorp-io","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Welcome to the Jungle","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Paris, France"],"countries":["FR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":55000,"max_usd":118000,"period":"year","method":"role_country_seniority_unknown","sample_n":107},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-10-06T10:10:04Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-18T07:27:48Z","board_verified":true,"closed_at":null,"days_open":11,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":11},"description":"Rejoins SCorp-io, une entreprise innovante dans le domaine de l'optimisation énergétique des bâtiments. En tant que Machine Learning Engineer / Data Scientist, tu développeras des modèles prédictifs pour comprendre, prévoir et optimiser le comportement thermique et énergétique des bâtiments. Tu travailleras en étroite collaboration avec l'équipe Produit & Tech, les développeurs, les experts CVC et les équipes opérationnelles.\nMissions\nConstruire des modèles prédictifs du comportement thermique des bâtiments, modéliser l’inertie, les vitesses de chauffage/refroidissement et les délais entre une commande et son effet.\nMesurer l’écart entre prédictions et observations, contribuer à l’évaluation des gains énergétiques et du ROI, quantifier l’incertitude et détecter les dérives.\nOptimiser horaires et consignes selon l’inertie, la météo et l’occupation, contribuer progressivement à l’optimisation des lois d’eau, lois d’air, PAC, CTA et réseaux hydrauliques.\nProfil recherché\nTu disposes d’une solide formation scientifique, data ou ingénieur et maîtrises plusieurs des domaines suivants :**Compétences clés**\n* Python, NumPy, pandas, SciPy, scikit-learn ou équivalents,\n* Machine Learning supervisé : régression linéaire, Ridge/Lasso, Random Forest, XGBoost/LightGBM ou équivalents,\n* Séries temporelles et forecasting,\n* Statistiques, feature engineering et validation de modèles,\n* Backtesting, MAE, RMSE, R² ou métriques adaptées,\n* Optimisation numérique.\n**Un vrai plus**\nUne expérience sur plusieurs de ces sujets constitue un vrai plus :\n* identification de systèmes, state-space models, filtres de Kalman,\n* modèles hybrides physique/data,\n* PyTorch ou Deep Learning appliqué aux séries temporelles,\n* Modelica, EnergyPlus ou outils de simulation,\n* MPC / optimal control / optimisation sous contraintes,\n* MLflow, MLOps et monitoring de modèles.\n*Les expériences dans le bâtiment, l’énergie, la thermique, l’industrie, la mécanique, l’IoT ou d’autres systèmes physiques nous intéressent particulièrement.*\n*Une expérience directe du CVC est appréciée, mais une forte expérience en modélisation de systèmes physiques dans un autre domaine peut être tout aussi 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