51 to 75 of 189 scikit-learn Jobs in England

Strong python programming skills as evidenced by earlier work in data science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

and deploying LLMs, with tools such as HuggingFace, Langchain, and OpenAI Extensive hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch Experience building production-grade machine learning deployments on AWS, Azure, or GCP Experience communicating and/or teaching technical concepts to non-technical and More ❯

Proven experience in translating technical methods to non-technical stakeholders Strong programming experience in python (R, Python, C++ optional) and the relevant analytics libraries (e.g., pandas, numpy, matplotlib, scikit-learn, statsmodels, pymc, pytorch/tf/keras, langchain) Experience with version control (GitHub) ML experience with causality, Bayesian statistics & optimization, survival analysis, design of experiments, longitudinal analysis, surrogate More ❯

frameworks. High-growth Experience: Prior experience working in high-growth environments, ideally start-ups or scale-ups Coding Skills: Proficient in Python, SQL, and one of Pytorch, Tensorflow, Scikit-learn, with daily experience in writing, debugging, and optimising code. ML Ops Knowledge: Familiarity with tools like MLflow, Kubeflow, or Vertex AI, and experience implementing CI/CD pipelines More ❯

knowledge of statistics. The Technology Systems are almost all running on Linux and most of the code is in Python, with the full scientific stack: numpy, scipy, pandas, scikit-learn to name a few of the open-source libraries we use extensively. We implement the systems that require the highest data throughput in Java and C++. We use More ❯

Enterprise AI Value Strategy Team Lead/Consultant Mid-Level Full time Job Role : Data & AI Consultant - R&D Location: London/Manchester/Edinburgh Career Level : Consultant Accenture is a leading global professional services company, providing a broad range More ❯

Enterprise AI Value Strategy Team Lead/Consultant Mid-Level Full time Job Role : Data & AI Consultant - R&D Location: London/Manchester/Edinburgh Career Level : Consultant Accenture is a leading global professional services company, providing a broad range More ❯

Senior Data Scientist – Behavioural Credit Modelling Hybrid working – London offices Up to £75,000 DOE + Benefits Ref: J12979 Are you passionate about building behavioural models that power smarter, fairer credit decisions? We’re working with a UK-based consumer More ❯

Senior Data Scientist – Behavioural Credit Modelling Hybrid working – London offices Up to £75,000 DOE + Benefits Ref: J12979 Are you passionate about building behavioural models that power smarter, fairer credit decisions? We’re working with a UK-based consumer More ❯

biggest names in private equity. Ideal Candidate Experience: 5+ years in data science roles, preferably in fast-moving or early-stage environments Languages & Tools: Strong Python (Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch) Advanced SQL AWS MLOps tooling (e.g., MLflow, SageMaker, or similar) Bonus Points For: Knowledge of LLMs, RAG pipelines, prompt engineering, or agentic interfaces Experience with More ❯

biggest names in private equity. Ideal Candidate Experience: 5+ years in data science roles, preferably in fast-moving or early-stage environments Languages & Tools: Strong Python (Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch) Advanced SQL AWS MLOps tooling (e.g., MLflow, SageMaker, or similar) Bonus Points For: Knowledge of LLMs, RAG pipelines, prompt engineering, or agentic interfaces Experience with More ❯

or a related field. Experience or coursework in machine learning, deep learning, or AI techniques (e.g., supervised learning, neural networks, NLP). Proficiency in Python and libraries like Scikit-learn, TensorFlow, PyTorch, or Hugging Face Transformers. Familiarity with data preprocessing, model evaluation, and deploying models in real-world environments. A passion for AI innovation and a desire to More ❯

or a related field. Experience or coursework in machine learning, deep learning, or AI techniques (e.g., supervised learning, neural networks, NLP). Proficiency in Python and libraries like Scikit-learn, TensorFlow, PyTorch, or Hugging Face Transformers. Familiarity with data preprocessing, model evaluation, and deploying models in real-world environments. A passion for AI innovation and a desire to More ❯

in statistics, probability, and machine learning fundamentals - either through a STEM degree, formal training, or self-study. Fluency in Python and SQL, including experience with libraries like Pandas, Scikit-learn, or equivalent. Demonstrated ability to solve real-world problems pragmatically using data. Clear, structured communication - especially the ability to explain complex topics simply. A growth mindset: curious, driven More ❯

data science related field (e.g. machine learning, statistics, mathematics) Proficiency in statistical data modelling techniques. Proficiency with Python, including experience with statistics/machine learning packages such as scikit-learn, pandas, numpy, etc. Good SQL/data manipulation skills required including cleaning and managing data. Experience in data visualisation and communication. Experience with working with raw datasets and More ❯

Bring: 3+ years' experience building and deploying ML models, ideally in NLP or computer vision domains. Expert-level Python and SQL, with solid experience using libraries like Pandas, Scikit-Learn, TensorFlow, etc. Proven experience working with BigQuery and big data pipelines on GCP . Deep understanding of statistics, machine learning algorithms, and data modelling. Strong analytical mindset with More ❯

Bring: 3+ years' experience building and deploying ML models, ideally in NLP or computer vision domains. Expert-level Python and SQL, with solid experience using libraries like Pandas, Scikit-Learn, TensorFlow, etc. Proven experience working with BigQuery and big data pipelines on GCP . Deep understanding of statistics, machine learning algorithms, and data modelling. Strong analytical mindset with More ❯

Machine Learning, AI, Statistics, Physics, Engineering, or a related field. Strong Python skills; Spark, SQL, and experience with large datasets is a plus. Proficiency with ML libraries (e.g. Scikit-learn, Pandas) and data visualisation tools. Curiosity and ambition to push the boundaries of data science in a commercial setting. Experience in retail or customer-focused analytics is highly More ❯

Machine Learning, AI, Statistics, Physics, Engineering, or a related field. Strong Python skills; Spark, SQL, and experience with large datasets is a plus. Proficiency with ML libraries (e.g. Scikit-learn, Pandas) and data visualisation tools. Curiosity and ambition to push the boundaries of data science in a commercial setting. Experience in retail or customer-focused analytics is highly More ❯

a model as a container, update an Airflow (or Azure Data Factory) job. Review: inspect dashboards, compare control vs. treatment, plan next experiment. Tech stack Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow)SQL (Redshift, Snowflake or similar)AWS SageMaker Azure ML migration, with Docker, Git, Terraform, Airflow/ADFOptional extras: Spark, Databricks, Kubernetes. What you'll bring More ❯

ability 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 More ❯

ability 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 More ❯

cycle processes and tools: CI/CD, version control (git), testing frameworks, MLOps Comfortable working with Docker and containerised applications Experience with data science Python libraries such as Scikit-learn, Pandas, NumPy, Pytorch etc. Experience using AWS or similar cloud computing platform Great communicator - convey complex ideas and solutions in clear, precise and accessible ways Team player who More ❯

years of experience applying data science in commercial settings Proven ability to lead data science projects from concept to production Strong Python skills (including libraries like Pandas, NumPy, Scikit-learn); experience with other languages is a plus Deep understanding of statistical modelling, predictive analytics, and optimisation techniques Comfortable working with cross-functional teams, including engineers, product leads, and More ❯

years of experience applying data science in commercial settings Proven ability to lead data science projects from concept to production Strong Python skills (including libraries like Pandas, NumPy, Scikit-learn); experience with other languages is a plus Deep understanding of statistical modelling, predictive analytics, and optimisation techniques Comfortable working with cross-functional teams, including engineers, product leads, and More ❯