Senior AI Engineer/Data Scientist

Emploi pas sur LinkedIn

🕒 il y a 7 jours

🇺🇸 États-Unis – Télétravail

⏰ Temps Plein

🟠 Senior

🚰 Ingénieur Data

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KANINI

Site WebLinkedInTous les Emplois

501 - 1000 employés

Fondée en 2003

💼 Conseil

🏥 Santé

📦 Logistique

Consulting • Healthcare • Logistics

KANINI est une entreprise mondiale d'ingénierie numérique et de services IT qui aide les entreprises à se transformer grâce à l'ingénierie produit, l'activation cloud, l'analyse de données et l'IA, l'automatisation intelligente et les solutions ServiceNow. Elle développe des applications cloud-native, modernise les systèmes anciens, met en œuvre des plateformes d'IA/ML et de données, et offre des résultats gérés via des modèles d'engagement flexibles pour les clients des secteurs de la santé, de la banque, de l'énergie, du gouvernement et de la technologie.

Description

• Lead end-to-end analysis of large, complex, multi-source datasets to surface patterns driving model inputs • Identify, collect, clean, validate, and transform all data required for prediction model consumption • Design and maintain scalable, production-grade data pipelines (training, validation, inference) • Perform deep EDA, data profiling, and quality audits to ensure model-ready data standards • Architect, train, evaluate, and iterate ML models — supervised, unsupervised, and reinforcement learning • Own feature engineering: selection, extraction, transformation, and dimensionality reduction • Apply advanced techniques: deep learning, NLP, time-series forecasting, ensemble methods • Benchmark, A/B test, and monitor models in production; drive continuous performance improvement • Deploy models via REST APIs (FastAPI/Flask); ensure reproducibility and scalability • Self-direct from problem definition through solution delivery with zero hand-holding • Translate ambiguous business problems into precise, executable data science problem statements • Communicate model results and data insights clearly to technical and non-technical stakeholders • Document all experiments, methodologies, and outcomes — audit-ready and reproducible • Champion best practices across the data science lifecycle; mentor junior team members

🎯 Exigences

• B.S./M.S./Ph.D. in Computer Science, Statistics, Mathematics, or equivalent quantitative field (Master's/Ph.D. strongly preferred) • 5+ years of hands-on data science experience with at least 2 years delivering production-grade ML models • Proven ability to own and deliver end-to-end data science projects independently • Portfolio demonstrating innovation in predictive modeling and measurable business impact • Kaggle rankings, research publications, or open-source ML contributions are a strong plus • Experience in a fast-paced, data-driven, decision-model environment • Statistics (Bayesian inference, hypothesis testing, regression, distributions) • Linear algebra, calculus, and probability applied to ML model design • Supervised & unsupervised learning, anomaly detection, clustering • Time-series analysis & forecasting: ARIMA, Prophet, LSTM • Python (Expert): NumPy, Pandas, Scikit-learn, Statsmodels, Matplotlib, Plotly • SQL (Advanced): window functions, CTEs, query optimization • Git / GitHub; CI/CD for ML; MLOps with MLflow or Kubeflow • Docker & Kubernetes for model containerization and serving • TensorFlow and/or PyTorch — deep learning architectures • XGBoost, LightGBM, CatBoost — gradient boosting & ensemble methods • Hugging Face Transformers — NLP, LLMs, and fine-tuning • SHAP, LIME — model explainability and interpretability • LLMs / Generative AI / Prompt Engineering — strong advantage • AWS (SageMaker, S3, Glue), GCP (Vertex AI, BigQuery), or Azure ML • Apache Spark / PySpark — distributed data processing • Airflow / Prefect — pipeline orchestration

🏖️ Avantages

• Health insurance • 401(k) matching • Flexible work hours • Paid time off • Remote work options