1 to 25 of 56 Remote XGBoost Jobs

anomalies. Engineer and select features for optimal model performance, leveraging domain understanding. Machine Learning & Statistical Modelling Implement both classical ML methods (regression, clustering, time-series forecasting) and advanced algorithms (XGBoost, LightGBM). Address computer vision, NLP, and generative tasks using PyTorch, TensorFlow, or Transformer-based models. Model Deployment & MLOps Integrate CI/CD pipelines for ML models using platforms like More ❯

people Willingness to travel. Want 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 and batch processing. Experience with More ❯

Owners and Colleagues alike. Essential Experience: Extensive experience designing, developing and deploying machine learning and AI solutions in production environments Statistical modelling, machine learning (e.g. logistic regression, random forest, XGBoost, and modern deep learning techniques (e.g. transformers, transfer learning, reinforcement learning) Proven ability to lead technical direction across projects or domains Expertise in model validation, explainability, governance and ethical AI More ❯

ll bring Solid understanding of 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 area (e.g. NLP, Transfer Learning etc) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc) Solid knowledge of SQL and Python's ecosystem for More ❯

mathematics, physics, or statistics. Experience in data science within financial services, insurance, or E-commerce (not essential). Familiarity with cloud-based deployment. Knowledge of neural networks, TensorFlow, CatBoost, XGBoost, SKlearn, or Pandas. Experience with API development. Proficiency in SQL. Background in software engineering or DevOps/MLOps. Understanding of CI/CD pipelines. Why Join Us? Join a forward More ❯

deliverables Qualifications Solid understanding of 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 and Python's ecosystem for More ❯

modelling, forecasting, and optimization. Strong proficiency in Python, SQL, and cloud-based data platforms (mainly Google Cloud and AWS). Experience with machine learning frameworks (e.g., TensorFlow, Scikit-learn, XGBoost). Strong stakeholder management skills, with the ability to communicate findings to non-technical teams and influence business strategy and commercial outcomes. Hands-on experience with pricing models, demand forecasting More ❯

Why loveholidays? At loveholidays, we're on a mission to open the world to everyone, giving our customers' unlimited choice, unmatched ease and unmissable value for their next getaway. Our team is the driving force behind our role as our More ❯

data-driven decision-making throughout 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 tools (e.g., Tableau). Exceptional More ❯

data-driven decision-making throughout 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 tools (e.g., Tableau). Exceptional More ❯

science or machine learning roles, preferably in a fast-paced tech or enterprise environment. Proficient in Python or R, SQL, and common machine learning frameworks (e.g., scikit-learn, TensorFlow, XGBoost, PyTorch). Strong foundation in statistics, probability, and hypothesis testing. Experience with cloud platforms (AWS, GCP, Azure) and big data tools (Spark, Hive, Databricks, etc.) is a plus. Excellent communication More ❯

tools, trends, and best practices Requirements Proven experience as a Machine Learning Engineer or similar role Strong proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, etc. Hands-on experience with data pipelines, model training, evaluation, and deployment Solid understanding of statistics, data structures, and algorithms Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and containerization More ❯

to lead end-to-end AI projects—from data exploration and model development to deployment and evaluation. Expert-level proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, PyTorch, and/or Hugging Face. Experience working with large-scale datasets, cloud platforms (AWS/GCP), and ML Ops tools is a plus. Excellent communication skills and the More ❯

to lead end-to-end AI projects—from data exploration and model development to deployment and evaluation. Expert-level proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, PyTorch, and/or Hugging Face. Experience working with large-scale datasets, cloud platforms (AWS/GCP), and ML Ops tools is a plus. Excellent communication skills and the More ❯

to lead end-to-end AI projects—from data exploration and model development to deployment and evaluation. Expert-level proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, PyTorch, and/or Hugging Face. Experience working with large-scale datasets, cloud platforms (AWS/GCP), and ML Ops tools is a plus. Excellent communication skills and the More ❯

to lead end-to-end AI projects—from data exploration and model development to deployment and evaluation. Expert-level proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, PyTorch, and/or Hugging Face. Experience working with large-scale datasets, cloud platforms (AWS/GCP), and ML Ops tools is a plus. Excellent communication skills and the More ❯

to lead end-to-end AI projects—from data exploration and model development to deployment and evaluation. Expert-level proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, PyTorch, and/or Hugging Face. Experience working with large-scale datasets, cloud platforms (AWS/GCP), and ML Ops tools is a plus. Excellent communication skills and the More ❯

of data science in finance, insurance or Ecommerce is an advantage but not required Experience of deployment in a cloud environment Experience with neural networks and Tensor Flow, CatBoost, XGBoost, SKlearn, Pandas Experience with API development SQL experience Software engineering experience DevOps/MLOps experience Good working understanding of CI/CD Diversity and flexible working at Hiscox At Hiscox More ❯

of data science in finance, insurance or Ecommerce is an advantage but not required ??Experience of deployment in a cloud environment ??Experience with neural networks and Tensor Flow, CatBoost, XGBoost, SKlearn, Pandas ??Experience with API development ??SQL experience ??Software engineering experience ??DevOps/MLOps experience ??Good working understanding of CI/CD If you hold the experience and technical skills More ❯

as Python, C# on Linux, Go, or similar. Utilise Advanced Technical Stack: Leverage our technical stack, including Python, SQL, PostgreSQL, BigQuery, and libraries such as Pandas, NumPy, SciKit-Learn, XGBoost, Matplotlib, and Pytorch, to develop robust and scalable AI/ML solutions. Strong foundation in data structures, algorithms, and problem-solving, with a clear focus on efficiency and optimisation. Experience More ❯

of data science in finance, insurance or Ecommerce is an advantage but not required Experience of deployment in a cloud environment Experience with neural networks and Tensor Flow, CatBoost, XGBoost, SKlearn, Pandas Experience with API development SQL experience Software engineering experience DevOps/MLOps experience Good working understanding of CI/CD If you hold the experience and technical skills More ❯

Required Skills and Qualifications MSc or PhD in Computer Science, Data Science, Machine Learning, or a related field. 3+ years of experience in developing machine learning solutions. Strong proficiency in Python and libraries such as TensorFlow, PyTorch, scikit-learn, XGBoost. More ❯

Solid understanding of supervised learning, time-series forecasting, clustering , and other core ML techniques. Strong experience with SQL and querying large, complex datasets. Proficiency in Python (Pandas, scikit-learn, XGBoost, etc.); familiarity with R or dbt is a plus. Experience working with BI tools such as Looker, Tableau, or Power BI. Comfort working in high-growth environments (start-ups or More ❯

Solid understanding of supervised learning, time-series forecasting, clustering , and other core ML techniques. Strong experience with SQL and querying large, complex datasets. Proficiency in Python (Pandas, scikit-learn, XGBoost, etc.); familiarity with R or dbt is a plus. Experience working with BI tools such as Looker, Tableau, or Power BI. Comfort working in high-growth environments (start-ups or More ❯

Solid understanding of supervised learning, time-series forecasting, clustering , and other core ML techniques. Strong experience with SQL and querying large, complex datasets. Proficiency in Python (Pandas, scikit-learn, XGBoost, etc.); familiarity with R or dbt is a plus. Experience working with BI tools such as Looker, Tableau, or Power BI. Comfort working in high-growth environments (start-ups or More ❯