1 to 25 of 693 scikit-learn Jobs in the UK

degree in Data Science, Computer Science, Statistics, Mathematics, or related fields (PhD a plus). Strong programming skills in Python, with proficiency in data science libraries (NumPy, Pandas, scikit-learn). Experience with SQL for data querying and analysis. Solid understanding of machine learning algorithms, statistical methods, and predictive modeling. Experience with NLP techniques for text analysis, classification More ❯

Aimpoint Digital is a premier analytics consulting firm with a mission to drive business value for clients through expertise in data strategy, data analytics, decision sciences, and data engineering and infrastructure. This position is within our decision sciences practice which More ❯

required). 3+ years of professional experience in machine learning engineering. 2+ years of leadership or technical mentoring experience. Strong expertise in Python for machine learning (Pandas, NumPy, scikit-learn, etc.). Experience with deep learning frameworks such as TensorFlow, PyTorch, or JAX. Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning). Experience building and maintaining … e.g., SHAP, LIME). Familiarity with reinforcement learning or generative AI models. Tools & Technologies: Languages: Python, SQL (optionally: Scala, Java for large-scale systems) ML Frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM MLOps: MLflow, Weights & Biases, Kubeflow, Seldon Core Data Processing: Pandas, NumPy, Apache Spark, Beam Model Serving: TensorFlow Serving, TorchServe, FastAPI, Flask Cloud Platforms: AWS (SageMaker, S3 More ❯

Machine Learning and AI tools to manage model lifecycles. Leverage cloud platforms like Azure, AWS, and GCP for scalable ML model deployment. Employ frameworks like TensorFlow, PyTorch, and scikit-learn for model development. Data Engineering and Preparation Oversee data ingestion, cleaning, transformation, and feature engineering processes to ensure high-quality datasets. Work with large datasets and implement scalable … Technical Skills Expertise in designing and deploying ML algorithms, AutoML tools, and AI applications. Proficiency with programming languages such as Python and R, and ML libraries (TensorFlow, PyTorch, scikit-learn). Hands-on experience with cloud platforms (Azure ML) and big data ecosystems (e.g., Hadoop, Spark). Strong understanding of CI/CD pipelines, DevOps practices, and infrastructure More ❯

Hands-on expertise building and deploying deep learning models (e.g., CNNs, Transformers, graph neural networks) in real-world applications. • Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). • Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). • Experience designing scalable ML infrastructure on cloud platforms (AWS SageMaker More ❯

Hands-on expertise building and deploying deep learning models (e.g., CNNs, Transformers, graph neural networks) in real-world applications. Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). Experience designing scalable ML infrastructure on cloud platforms (AWS SageMaker More ❯

DEPT/AI has a single mission: to make the best work in the industry using AI to enhance everything we do. This role sits within our EMEA Data & AI practice, which has deep expertise in leveraging AI. The team More ❯

and visualization tools to support AI model insights. · Write clean, well-documented, and secure code using Python, C C#, or R. · Leverage libraries such as TensorFlow, PyTorch, and Scikit-learn to prototype and optimize AI models. · Communicate complex technical concepts clearly to both technical and non-technical stakeholders. · Stay up-to-date with emerging AI trends, threat intelligence More ❯

AI/ML algorithms and techniques, including LLMs, GenAI, and automated AI systems. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and data science libraries (e.g., NumPy, pandas, scikit-learn). Proficiency in Python, R, or other relevant programming languages. Proficiency in working with large datasets, data wrangling, and data preprocessing. Experience in data science, statistical modelling, and More ❯

Master’s degree from a Russell Group university in Data Science, Computer Science, Mathematics, Physics, Engineering, or a related discipline Strong programming skills in Python (e.g. NumPy, pandas, scikit-learn, matplotlib); R is also welcome A solid understanding of core machine learning concepts, data wrangling, and model evaluation Proficiency with SQL and experience handling large datasets A passion More ❯

Master’s degree from a Russell Group university in Data Science, Computer Science, Mathematics, Physics, Engineering, or a related discipline Strong programming skills in Python (e.g. NumPy, pandas, scikit-learn, matplotlib); R is also welcome A solid understanding of core machine learning concepts, data wrangling, and model evaluation Proficiency with SQL and experience handling large datasets A passion More ❯

Master’s degree from a Russell Group university in Data Science, Computer Science, Mathematics, Physics, Engineering, or a related discipline Strong programming skills in Python (e.g. NumPy, pandas, scikit-learn, matplotlib); R is also welcome A solid understanding of core machine learning concepts, data wrangling, and model evaluation Proficiency with SQL and experience handling large datasets A passion More ❯

/GenAI prompting and augmentation for textual analysis, with an interest in learning more. Experience working with commonly used data science libraries and frameworks, e.g. Spacy, pandas, numpy, scikit-learn, Keras/TensorFlow, PyTorch, LangChain, Huggingface transformers etc. Familiar with both on-premises and cloud-based platforms (e.g. AWS). Working understanding of ML Ops workflows and ability More ❯

messy datasets using Python. Practical expertise and work experience with ML projects, both supervised and unsupervised. Proficient programming skills with Python, including libraries such as NumPy, pandas, and scikit-learn, as well as R. Understanding and usage of the OpenAI API. NLP: tokenization, embeddings, sentiment analysis, basic transformers for text-heavy datasets. Experience with LLM & Prompt Engineering, including More ❯

such as RAG, LangChain, TensorFlow, and PyTorch. Exposure to LLMs from model families such as Anthropic, Meta, Amazon, and OpenAI. Familiarity with tools and packages like Pandas, NumPy, scikit-learn, Plotly/Matplotlib, and Jupyter Notebooks. Knowledge of ML-adjacent technologies, including AWS SageMaker and Apache Airflow. Proficiency in data pre-processing, data wrangling, and augmentation techniques. Experience More ❯

Senior Data Analyst at The Hawker’s Club Are you a qualified and experienced Data Analyst looking for a hugely rewarding role to further your career? Look no further! The Hawker’s Club is currently recruiting to fill the role More ❯

Grow with us. We are looking for a Machine Learning Engineer to work along the end-to-end ML lifecycle, alongside our existing Product & Engineering team. About Trudenty: The Trudenty Trust Network provides personalised consumer fraud risk intelligence for fraud More ❯

and deployment hurdles. Ability to translate business questions into analytical frameworks and interpret results for non-technical stakeholders. Strong proficiency in Python, SQL, and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Experience with model operationalization using tools like Docker, Kubernetes, MLflow, or SageMaker. Marketing KPIs knowledge: CTR, conversion rate, MQL to SQL, ROI, CLV, CAC, retention. … social media, and paid media. Excellent problem-solving skills, business acumen, and the ability to translate complex models into actionable insights for non-technical stakeholders. Tools/Frameworks : Scikit-learn, XGBoost, LightGBM, StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy … matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses (e.g., Snowflake, Databricks, Redshift, BigQuery) Familiarity with data pipelines and orchestration tools like Airflow Work closely with Data Engineers to ensure model-ready data and More ❯

and deployment hurdles. Ability to translate business questions into analytical frameworks and interpret results for non-technical stakeholders. Strong proficiency in Python, SQL, and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Experience with model operationalization using tools like Docker, Kubernetes, MLflow, or SageMaker. Marketing KPIs knowledge: CTR, conversion rate, MQL to SQL, ROI, CLV, CAC, retention. … social media, and paid media. Excellent problem-solving skills, business acumen, and the ability to translate complex models into actionable insights for non-technical stakeholders. Tools/Frameworks : Scikit-learn, XGBoost, LightGBM, StatsModels PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy … matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses (e.g., Snowflake, Databricks, Redshift, BigQuery) Familiarity with data pipelines and orchestration tools like Airflow Work closely with Data Engineers to ensure model-ready data and More ❯

and deployment hurdles. Ability to translate business questions into analytical frameworks and interpret results for non-technical stakeholders. Strong proficiency in Python, SQL, and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Experience with model operationalization using tools like Docker, Kubernetes, MLflow, or SageMaker. Marketing KPIs knowledge: CTR, conversion rate, MQL to SQL, ROI, CLV, CAC, retention. … PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses More ❯

and deployment hurdles. Ability to translate business questions into analytical frameworks and interpret results for non-technical stakeholders. Strong proficiency in Python, SQL, and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Experience with model operationalization using tools like Docker, Kubernetes, MLflow, or SageMaker. Marketing KPIs knowledge: CTR, conversion rate, MQL to SQL, ROI, CLV, CAC, retention. … PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses More ❯

and deployment hurdles. Ability to translate business questions into analytical frameworks and interpret results for non-technical stakeholders. Strong proficiency in Python, SQL, and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Experience with model operationalization using tools like Docker, Kubernetes, MLflow, or SageMaker. Marketing KPIs knowledge: CTR, conversion rate, MQL to SQL, ROI, CLV, CAC, retention. … PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses More ❯

and deployment hurdles. Ability to translate business questions into analytical frameworks and interpret results for non-technical stakeholders. Strong proficiency in Python, SQL, and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Experience with model operationalization using tools like Docker, Kubernetes, MLflow, or SageMaker. Marketing KPIs knowledge: CTR, conversion rate, MQL to SQL, ROI, CLV, CAC, retention. … PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses More ❯

and deployment hurdles. Ability to translate business questions into analytical frameworks and interpret results for non-technical stakeholders. Strong proficiency in Python, SQL, and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Experience with model operationalization using tools like Docker, Kubernetes, MLflow, or SageMaker. Marketing KPIs knowledge: CTR, conversion rate, MQL to SQL, ROI, CLV, CAC, retention. … PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses More ❯

and deployment hurdles. Ability to translate business questions into analytical frameworks and interpret results for non-technical stakeholders. Strong proficiency in Python, SQL, and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Experience with model operationalization using tools like Docker, Kubernetes, MLflow, or SageMaker. Marketing KPIs knowledge: CTR, conversion rate, MQL to SQL, ROI, CLV, CAC, retention. … PyCaret, Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses More ❯