1 to 25 of 36 XGBoost Jobs

to stand out? Consulting Experience Databricks Machine Learning Associate or Machine Learning Professional Certification. Familiarity with traditional machine learning tools such as Python, SKLearn, XGBoost, SparkML, etc. Experience with deep learning frameworks like TensorFlow or PyTorch. Knowledge of ML model deployment options (e.g., Azure Functions, FastAPI, Kubernetes) for real-time More ❯

computer science fundamentals, including data structures, algorithms, data modelling, and software architecture Solid understanding of classical Machine Learning algorithms (e.g., Logistic Regression, Random Forest, XGBoost, etc.), state-of-the-art research areas (e.g., NLP, Transfer Learning, etc.), and modern Deep Learning algorithms (e.g., BERT, LSTM, etc.) Solid knowledge of SQL More ❯

with large datasets and with streaming data architectures. Demonstrated knowledge of large language models and generative AI. ML frameworks (Scikit-Learn, NumPy, SciPy, Pandas, XGBoost, Tensorflow, PyTorch, MXNet, LLM) Qualifications Required: Bachelor's Degree with 7 years experience; Master's Degree with 6 years experience; PhD with 2 years experience. More ❯

years technical experience. Relevant experience may be accepted in place of a degree Experience with various machine learning libraries (i.e. Apache Spark, Scikit-learn, XGBoost, etc.) Experience with various data manipulation and pipeline libraries (i.e. Pandas, Polars, matplotlib, Plotly, numpy, scipy, etc.)Experience with data science environments (e.g. Jupyter Notebook More ❯

unstructured data eg. Text, PDFs, jpgs, call recordings, video, etc. Knowledge of machine learning modelling techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate More ❯

unstructured data eg. Text, PDFs, jpgs, call recordings, video, etc. Knowledge of machine learning modelling techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate More ❯

unstructured data eg. Text, PDFs, jpgs, call recordings, video, etc. Knowledge of machine learning modelling techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate More ❯

trees, and other traditional machine learning models, translating conceptual ideas into actual solutions. Fluent in some of these machine learning frameworks such as SKLearn, XGBoost, PyTorch, or Tensorflow. Proficient in Python and able to transform abstract machine learning concepts into robust, efficient, and scalable solutions. Strong Computer Science fundamentals and More ❯

to the design, development, testing, and deployment of data science and AI solutions Experience and understanding of applied machine learning techniques in Python (e.g., xgboost, regression, decision trees) Experience with physics modelling highly desirable Practical knowledge and experience of developing AI solutions using advanced machine learning techniques (e.g., reinforcement learning More ❯

the organisation. Required Skills & Experience: Extensive experience conducting data analysis within relevant sectors. Proficiency in SQL, Python, and data science libraries such as sklearn, XGBoost, and CatBoost. Experience with Azure, MLOps, and API frameworks. Strong understanding of statistical methods, probability theory, pricing optimisation, and elasticity modelling. Expertise with data visualisation More ❯

Python and SQL through Git, and applying other best practices to technical projects Experience and understanding of applied machine learning techniques in Python (e.g. xgboost, regression, decision trees) Practical knowledge and experience of developing AI solutions using advanced machine learning techniques (e.g. reinforcement learning, deep learning, LLMs) Experience of using More ❯

and technologies that are approved for use in government environments. Machine Learning. We apply best-of-breed machine learning frameworks (TensorFlow, scikit-learn, and xgboost) to create and deploy machine learning models for classification and regression. In addition to standard supervised machine learning techniques, we bring unique instance-based learning More ❯

NBA and MLB (ideally) Advanced knowledge of statistical modelling and machine learning, with relevant experience in the use of Python libraries (e.g., scikit-learn, xgboost, tensorflow, pymc3, statsmodels) and R equivalents Experience generating reports, dashboards, and data visualisations SQL experience as well as experience working with relational databases Excellent presentation More ❯

track record delivering ML/AI solutions in complex, real-world environments Strong Python skills and experience with key ML libraries (e.g., scikit-learn, XGBoost, PyTorch) Exposure to Generative AI technologies (e.g., LLMs, embeddings, RAG systems) Excellent communication skills and ability to engage senior stakeholders Nice to Have: Experience in More ❯

track record delivering ML/AI solutions in complex, real-world environments Strong Python skills and experience with key ML libraries (e.g., scikit-learn, XGBoost, PyTorch) Exposure to Generative AI technologies (e.g., LLMs, embeddings, RAG systems) Excellent communication skills and ability to engage senior stakeholders Nice to Have: Experience in More ❯

While they use a range of technologies internally, familiarity with open-source tools is highly valued. Languages & Libraries: Python, R, Pandas, NumPy, scikit-learn, XGBoost, LightGBM, statsmodels NLP & ML Frameworks: spaCy, Hugging Face Transformers, TensorFlow or PyTorch (where applicable) Data Engineering & Pipelines: dbt, Airflow, SQL, Spark, Dask Visualisation: Plotly, Seaborn More ❯

While they use a range of technologies internally, familiarity with open-source tools is highly valued. Languages & Libraries: Python, R, Pandas, NumPy, scikit-learn, XGBoost, LightGBM, statsmodels NLP & ML Frameworks: spaCy, Hugging Face Transformers, TensorFlow or PyTorch (where applicable) Data Engineering & Pipelines: dbt, Airflow, SQL, Spark, Dask Visualisation: Plotly, Seaborn More ❯

basics but by year 3 should be quite proficient with at least pulling data Data Lake implementation and processing. Knowledge of Snowflake or equivalent XGBoost, LightGBM and the ability to use them for tabular data NLP and familiar with modern transformers Experience using Tableau or PowerBI for visualisations and reporting More ❯

new tools and packages where appropriate. Desired Skills & Experience: Core Technical Skills Expert in Python, SQL, and modern data science toolkits (e.g. scikit-learn, XGBoost, statsmodels). Solid grasp of dbt for data transformation. Experience with modern cloud data stacks - Snowflake, BigQuery, Redshift, etc. Comfortable working in agile environments with More ❯

ML : Very strong knowledge in classical ML algorithms for regression & classification, supervised and unsupervised machine learning, both theoretical and practical (e.g. using scikit-learn, xgboost) ML niche: Expertise in at least one of the following ML specialisations: Timeseries forecasting/Natural Language Processing/Computer Vision Deep Learning: Good knowledge More ❯

ML : Very strong knowledge in classical ML algorithms for regression & classification, supervised and unsupervised machine learning, both theoretical and practical (e.g. using scikit-learn, xgboost) ML niche: Expertise in at least one of the following ML specialisations: Timeseries forecasting/Natural Language Processing/Computer Vision Deep Learning: Good knowledge More ❯

categorical, image, to speech data. We operate at large scale and rely on state-of-the-art modeling techniques to power our ML models: XGBoost, BERT, Vision Transformers, Large Language Models. Minimum Qualifications 2+ years of building models for business application experience. PhD, or Master's degree and 4+ years More ❯

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 and More ❯

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 and More ❯

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 and More ❯