20 of 20 XGBoost Jobs

feature selection using techniques such as PCA, Lasso, and Elastic Net. Develop and deploy models using algorithms like Linear & Logistic Regression, SVM, Decision Trees, XGBoost, Hist Gradient Boosting, and LightGBM. Utilize cloud technologies (AWS, Azure, Hadoop) for scalable data solutions. Create interactive dashboards and visualizations using tools like Domo, Tableau ...

Kinesis, EMR Machine Learning Supervised Models: Naïve Bayes, Logistic Regression, SVM, Linear Regression, KNN Tree-Based Models: Decision Trees, Random Forest, Gradient Boosted Trees, XGBoost Unsupervised Models: K-Means, DBSCAN, Hierarchical Clustering Deep Learning ANN, CNN, RNN, LSTM Frameworks: TensorFlow (Gradient Tape), PyTorch NN, Keras Sequential NLP & Generative AI NLTK ...

language) and in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data ...

Lambda, Redshift). Proven ability to design and implement machine learning algorithms and models. Proficiency in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow). Strong data analysis, statistical modelling, and experimentation skills. Experience with data visualisation tools and techniques. Proficiency in programming languages such as Python ...

algorithms, data structures, multithreading. Experience working with Generative AI, using LangChain for Gen AI and techniques like RAG. Experience using ML and DL Libraries: XGBoost, SKlearn, Tensorflow or PyTorch. In depth experience building solutions using public clouds such as AWS, GCP. Experience using ML platforms like SageMaker, H2O, DataRobot, etc. ...

Statistical Modeling: Deep knowledge of predictive modeling techniques such as regression, time series forecasting (e.g., ARIMA, Prophet), causal inference, and machine learning methods (e.g., XGBoost, LightGBM). Experimental Design: Proficiency in designing and analyzing experiments, including A/B testing, geo-lift studies, and other causal frameworks to measure ...

Data Science or Data Engineering. Experience with NLP for entity and relationship extraction is a plus. Strongly skilled in standard ML workflows (Scikit-Learn, XGBoost) Experience with geospatial data (GIS, GeoPandas) is valued. Education MSc in Computer Science, Data Science, or a related engineering field (PhD welcome, but practical delivery ...

will have the following skills and experience: Strong Python and SQL experience, ideally with Databricks Experience with ML and LLM tools (e.g., scikit learn, XGBoost, PyTorch, TensorFlow, RAG, APIs) Comfortable with data engineering basics and MLOps practices Experience working with cloud platforms such as Azure, AWS, or GCP Benefits ...

modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with ...

/Azure acceptable) Experience with GenAI/LLMs (commercial or serious hands-on experimentation) Strong Python and modern ML tooling (e.g. scikit-learn, XGBoost, TensorFlow or PyTorch) Clear interest in crypto/blockchain (commercial experience a plus, curiosity required) Responsibilities Build, train, and improve ML models on large-scale blockchain ...

experience in ML/AI engineering or data science roles (senior level). Hands-on experience with Python ML frameworks (scikit-learn, PyTorch, TensorFlow, XGBoost). Experience with AWS ML services (SageMaker, Lambda, S3) and MLOps/LLMOps tooling (MLflow, Weights & Biases, DVC). Proven track record transitioning models from ...

reliable, scalable systems. Essential experience includes: Commercial experience developing and deploying machine learning models in Python Proficiency with ML frameworks such as scikit-learn, XGBoost, PyTorch or TensorFlow Strong experience delivering ML solutions using AWS services (e.g. SageMaker, Lambda, S3) Expertise in experiment design, including hypothesis formulation, A/ ...

recommendation systems, optimisation, or marketing analytics. Deep understanding of ML fundamentals across the full lifecycle. Hands‐on experience with ML frameworks (scikit‐learn, XGBoost, PyTorch, TensorFlow, LightGBM). Strong exposure to LLMs, prompt engineering, RAG, and evaluation frameworks. Familiarity with MLOps tooling: CI/CD, experiment tracking, model registries, monitoring ...

MLOps, or data engineering. Strong programming skills in Python and familiarity with Java is a plus. Experience with ML frameworks and libraries (PyTorch, TensorFlow, XGBoost, LightGBM, etc.). Solid knowledge of distributed data processing systems (Spark, Flink, Hadoop) and query languages (SQL/HiveQL). Hands on experience with ...

into business workflows. Desirable: Background in quantitative disciplines (math, stats, physics). Experience in finance, insurance, or ecommerce. Familiarity with ML frameworks like TensorFlow, XGBoost, and SKLearn. If this sounds like something you are interested in, please get in contact: thomas.deakin@spgresourcing.com SPG Resourcing is an equal opportunities employer ...

colleagues and share learnings across the team. Requirements: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow. Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments. Strong experiment design skills: hypothesis formulation, A/B testing ...

modern AWS production environments. The Technical Reality: We operate a pragmatic stack where Linear Regression remains vital for stability and baseline performance, while XGBoost and LLMs are used as responsible additions. We are looking for someone who knows when to use a simple linear model and when to deploy … Responsibilities Predictive Modelling (Linear & Non-Linear): You will build and maintain foundational Linear Regression models for credit, affordability, and fraud scoring, while developing advanced XGBoost models for deeper risk insights. You will mine data to find behavioural signals—such as spending volatility or income stability—that predict affordability, repayment ...

EXPERIENCE The successful Lead Data Scientist will have the following skills and experience: Strong proficiency in python, SQL Machine Learning experience Search, Recommendation, Personalisation, XGBoost, Two Tower or Neural Network experience Managerial experience preferred Strong communication skills HOW TO APPLY Please register your interest by sending your CV to Giulia ...

EXPERIENCE The successful Lead Data Scientist will have the following skills and experience: Strong proficiency in python, SQL Machine Learning experience Search, Recommendation, Personalisation, XGBoost, Two Tower or Neural Network experience Managerial experience preferred Strong communication skills HOW TO APPLY Please register your interest by sending your CV to Giulia ...

banking applications such as fraud detection and credit risk. Deep, hands-on experience with a variety of modeling techniques, including: Classification models: (e.g., CatBoost, XGBoost, LightGBM) Regression models: (e.g., CatBoost, XGBoost, LightGBM) Time Series Analysis: (e.g., ARIMA, SARIMA, Prophet) Unsupervised Models: (e.g., clustering, anomaly detection such as Isolation Forest) Strong ...