26 to 50 of 72 XGBoost Jobs

Machine Learning systems from research all the way to deployment and maintenance Excellent Python programming skills and hands on experience with libraries such as XGBoost, PyTorch, TensorFlow, or Hugging Face Experience working with cloud‐based data and AI platforms, particularly Azure and Databricks Strong stakeholder engagement and storytelling capabilities; able ...

Machine Learning systems from research all the way to deployment and maintenance* Excellent Python programming skills and hands on experience with libraries such as XGBoost, PyTorch, TensorFlow, or Hugging Face* Experience working with cloud-based data and AI platforms, particularly Azure and Databricks* Strong stakeholder engagement and storytelling capabilities; able ...

instinct for leakage, bias and overfitting in messy real-world data. Strong ML fundamentals across classical ML and deep learning (pandas, scikit-learn, XGBoost or LightGBM, PyTorch), and the judgement to tell when a language model is the wrong tool. A deep understanding of how modern models are built ...

time data is a strong plus Also desirable: experience with vector databases (pgvector, Pinecone, Weaviate, Qdrant or similar); machine learning experience (PyTorch, TensorFlow, XGBoost/LightGBM) for building, maintaining and extending existing models; Bloomberg API integration to Python; data science/statistics, signal research, backtesting or factor modelling; full stack ...

SQL. Expert-level experience with the Python data stack (pandas, NumPy, scikit-learn) and at least one major ML framework (such as PyTorch or XGBoost/LightGBM) Systemic Problem Solving: Ability to work on unique issues requiring conceptual thinking and broad impact. You know how to build for long-term ...

opportunities to improve business efficiency through intelligent automation. Skills & Experience Strong Python programming skills with experience using libraries such as Pandas, NumPy, Scikit-learn, XGBoost or LightGBM, and Plotly, Rust, Typescript, Python or Matplotlib. Experience developing machine learning models for prediction, optimisation or classification. Strong SQL skills and experience working ...

machine learning services using one or more cloud providers (preferably GCP). Excellent understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.). Strong knowledge of SQL and Python’s ecosystem for data analysis (Jupyter, Pandas, Scikit ...

Airflow, Prefect, etc.). Hands-on experience with common ML libraries and frameworks, e.g. PyTorch, scikit-learn, and gradient-boosting libraries such as XGBoost or LightGBM. Clear evidence of practical experience defining and deploying containerised systems, i.e.: Implementing APIs for internal services, e.g. via FastAPI; Deploying containerised systems to production ...

/agent experience (tool use/function calling, RAG, structured outputs, guardrails) in production. Strong Python + ML stack (e.g., scikit-learn, XGBoost/LightGBM, PyTorch/TF) and solid experiment/evaluation skills. Strategic Problem Solver: You approach ambiguous problems by deeply understanding business context and framing data science ...

based machine learning services using one or more cloud providers (preferably GCP) Excellent understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) < Strong knowledge of SQL and Python's ecosystem for data analysis (Jupyter, Pandas, Scikit Learn ...

science, machine learning, pricing analytics, or commercial operations. Strong technical background in Python or R, SQL, and applied ML libraries (e.g. scikit-learn, TensorFlow, XGBoost). Experience with data visualization tools (Tableau, Power BI, or similar). Understanding of statistical methods, supervised/unsupervised learning, and business experimentation. Ability ...

report what the results do and don’t show Strong Python skills and hands‐on experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, PyTorch or Keras Solid grounding in modern software engineering, infrastructure and data tooling, and an understanding of the MLOps challenges across the full ML lifecycle. ...

report what the results do and don’t show Strong Python skills and hands‐on experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, PyTorch or Keras Solid grounding in modern software engineering, infrastructure and data tooling, and an understanding of the MLOps challenges across the full ML lifecycle. ...

Deep expertise with AWS SageMaker (training, processing, pipelines, endpoints, registry) and complementary AWS services. Expert-level Python and ML Model frameworks (e.g. PyTorch, TensorFlow, XGBoost).Strong thought leadership in MLOps automation, CI/CD for ML, and model lifecycle management. Advanced experience designing explainability systems, reason codes, and governance artefacts. ...

cards, or BNPL. Deep proficiency in PD model development and validation, with strong knowledge of calibration techniques. Advanced Python and SQL skills; familiar with XGBoost, scikit-learn, pandas, MLFlow. Experience with explainability frameworks such as SHAP, LIME, PDP. Ability to communicate technical concepts clearly and influence cross-functional decisions. Familiarity ...

cards, or BNPL. Deep proficiency in PD model development and validation, with strong knowledge of calibration techniques. Advanced Python and SQL skills; familiar with XGBoost, scikit-learn, pandas, MLFlow. Experience with explainability frameworks such as SHAP, LIME, PDP. Ability to communicate technical concepts clearly and influence cross-functional decisions. Familiarity ...

Hands-on experience building credit scorecards or risk models (application, behavioural, or collections) Proficiency in Python and SQL; experience with ML frameworks (scikit-learn, XGBoost, etc.) Familiarity with Open Banking data, bureau data (Experian, Equifax, TransUnion), or alternative data sources Comfort working in small, fast-moving teams where you have ...

ranking, NLP, or LLMs). Data & ML Ecosystem: Familiarity with data processing libraries (e.g. Pandas, NumPy) and ML frameworks (e.g. PyTorch, Scikit-learn or XGBoost). Growth Mindset: A genuine interest in distributed system and software design, concurrent algorithms, data structures, and software engineering. At Quantcast, we craft offers that ...

Deep expertise with AWS SageMaker (training, processing, pipelines, endpoints, registry) and complementary AWS services. Expert‐level Python and ML Model frameworks (e.g. PyTorch, TensorFlow, XGBoost). Strong thought leadership in MLOps automation, CI/CD for ML, and model lifecycle management. Advanced experience designing explainability systems, reason codes, and governance ...

professional environment. Programming Fluency: Strong programming skills in Python and SQL plus specific proficiency with the PyData stack (pandas, numpy, scikit-learn, XGBoost, LightGBM) Domain Knowledge: A strong understanding of cricket and the factors influencing match state outcomes (win probability, expected metrics like xR/xW) Tracking Data Experience: Prior ...

platforms.Model Development, Governance & Explainability* Deep experience applying a wide range of machine learning techniques to real-world business problems, including using models such as XGBoost and deep learning approaches using frameworks such as PyTorch or TensorFlow, taking solutions from experimentation through to production deployment and operation.* Strong knowledge of model ...

Model Development, Governance & Explainability Deep experience applying a wide range of machine learning techniques to real‐world business problems, including using models such as XGBoost and deep learning approaches using frameworks such as PyTorch or TensorFlow, taking solutions from experimentation through to production deployment and operation. Strong knowledge of model ...

platforms. Model Development, Governance & ExplainabilityDeep experience applying a wide range of machine learning techniques to real-world business problems, including using models such as XGBoost and deep learning approaches using frameworks such as PyTorch or TensorFlow, taking solutions from experimentation through to production deployment and operation. Strong knowledge of model ...

libraries such as Pandas Strong understanding of Machine Learning, including both supervised and unsupervised techniques Experience with tree-based and boosting algorithms such as XGBoost, LightGBM, and related approaches Experience working with large-scale datasets, ideally using PySpark Strong analytical and SQL skills for handling, querying, and interpreting complex datasets ...

with enterprise‐level infrastructure and architecture, including a extensive understanding of APIs. You have experience building, tuning, or deploying ML models (e.g., scikit‐learn, XGBoost, PyTorch, TensorFlow) in production settings Experience with AI use cases in financial services (e.g. fraud detection, credit scoring, underwriting) is a strong plus. ...