1 to 25 of 145 Permanent scikit-learn Jobs in England

or related field. 7+ years of professional software development experience, with at least 3 years in AI/ML. Strong proficiency in Python , including libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch . Solid understanding of ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . 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 ❯

analytics to generate business value and change, including optimisation of production processes Great knowledge of Python and in particular, the classic Python Data Science stack (NumPy, pandas, PyTorch, scikit-learn, etc) is required; Familiarity with PySpark is also desirable. A cloud platform experience (e.g Azure, AWS, GCP), we're using Azure in the team. Good SQL understanding in 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 ❯

ML projects, including initial conceptualization, data handling, model development, and deployment. Proficiency in programming languages, including Python. Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-Learn, etc. Experience developing Python APIs using tools such as FastAPI. Knowledge of database technologies (SQL, MongoDB, Databricks) and data pipeline tools. Familiar with ML CI/CD pipelines More ❯

QISQL for data access and processing (PostgreSQL preferred, but general SQL knowledge is important) Latest Data Science platforms (e.g., Databricks, Dataiku, AzureML, SageMaker) and frameworks (e.g., TensorFlow, MXNet, scikit-learn) Software engineering practices (coding standards, unit testing, version control, code review) Hadoop distributions (Cloudera, Hortonworks), NoSQL databases (Neo4j, Elastic), streaming technologies (Spark Streaming) Data manipulation and wrangling techniques More ❯

frameworks. Skills and Qualifications Skills & Expertise Strong experience in machine learning, deep learning, and statistical analysis. Expertise in Python, with proficiency in ML and NLP libraries such as Scikit-learn, TensorFlow, Faiss, LangChain, Transformers and PyTorch. Experience with big data tools such as Hadoop, Spark, and Hive. Familiarity with CI/CD and MLOps frameworks for building end More ❯

Experience: Proven ability to solve complex, real-world problems through data science and analytics. Experience coaching and reviewing work of junior team members. Strong Python skills (pandas, numpy, scikit-learn) and a solid grounding in probability and statistics. Deep knowledge of machine learning methods and their practical application. Experience managing multiple end-to-end data science projects across More ❯

Experience: Proven ability to solve complex, real-world problems through data science and analytics. Experience coaching and reviewing work of junior team members. Strong Python skills (pandas, numpy, scikit-learn) and a solid grounding in probability and statistics. Deep knowledge of machine learning methods and their practical application. Experience managing multiple end-to-end data science projects across More ❯

at scale. Deep familiarity with core ML concepts (classification, time-series, statistical modeling) and their real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from More ❯

a compelling and actionable way Mentor junior data scientists and contribute to the evolution of data science standards and practices Key Skills & Technologies Technical: Strong in Python (pandas, scikit-learn, XGBoost, LightGBM, etc.) Proficient in SQL for complex customer data extraction and manipulation Experience with customer analytics techniques: segmentation, RFM analysis, clustering, time-series, A/B testing More ❯

Machine Learning Fundamentals: A solid understanding of machine learning concepts, including algorithms, data pre-processing, model evaluation, and training. Familiarity with ML frameworks such as TensorFlow, PyTorch, and scikit-learn is beneficial. DevOps Practices: Experience with DevOps practices, including continuous integration and continuous deployment (CI/CD), containerisation using Docker, and Infrastructure as Code (IaC) methodologies. Cloud Platforms More ❯

desirable Strong expertise in Generative AI, LLMs, or related NLP technologies Proficiency in Python and/or Scala; experience with ML libraries such as TensorFlow, PyTorch, HuggingFace, or scikit-learn Experience with Databricks, distributed data systems (e.g., Spark, Hadoop), and cloud platforms (AWS, GCP, or Azure) Ability to thrive in ambiguous environments, working closely with cross-functional teams More ❯

to real-world use cases Contribute to code reviews, testing, and documentation Required Skills & Experience Strong proficiency in Python and its AI/ML ecosystem (e.g. NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow) Experience with machine learning model development , training, and deployment Familiarity with LLMs , NLP , or computer vision techniques Solid understanding of software engineering principles and version control More ❯

priorities, meet deadlines, and deliver on project objectives. Masters degree in a STEM field (maths, science, engineering etc.) or equivalent Strong programming skills in Python (e.g., NumPy, Pandas, scikit-learn, TensorFlow/PyTorch). Demonstrable experience in creating and developing Python libraries. Demonstrable experience designing, implementing and training machine learning models from scratch. Strong foundations in applied mathematics More ❯

related field. 5+ years' experience in data science, with proven success delivering end-to-end projects. Proficiency in Python, R, SQL, and machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Strong skills in data storytelling, problem-solving, and working with both structured and unstructured data. Experience with APIs, cloud platforms (especially AWS), and MLOps practices is a More ❯

of LLM outputs Good foundational knowledge of standard machine learning algorithms and statistical methods Proficient in python and its standard data science and ML libraries (e.g., numpy, pandas, scikit-learn, boosting and data visualization libraries) Practical experience in data processing and feature engineering for machine learning applications Ability to write robust production-ready Python code Good communication and More ❯

previous management or mentorship. Have good communication skills. Nice to have Experience deploying LLMs and agent-based systems Our technology stack Python and associated ML/DS libraries (scikit-learn, numpy, pandas, LightGBM, LangChain/LangGraph, TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, ECR, Athena, etc. MLOps: Terraform, Docker, Spacelift, Airflow, MLFlow Monitoring: New Relic CI/ More ❯

/ML, LLMs, and GenAI technologies. To Be Successful in The Role, You Will Have Required Skills: Proficiency in Python and machine learning framework s (e.g., TensorFlow, PyTorch, Scikit-Learn). Hands-on experience with OpenAI APIs and Microsoft Azure AI services. Strong understanding of large language models, prompt engineering, and fine-tuning techniques. Experience with data manipulation More ❯

from structured and unstructured datasets of varying sizes. Track record of implementing impactful models that drive sustained business results. Proficiency in the Python data science tech stack (pandas, scikit-learn, NumPy, and visualisation libraries) Experience working in a Linux-based cloud environment (e.g. GCP, Azure, AWS). Experience using git version control. Communication, stakeholder management, and problem-solving More ❯

implementing efficient data pipelines, including performance tuning and optimization. Proven ability to apply machine learning techniques to real-world problems. Familiarity with core libraries such as Pandas, NumPy, Scikit-learn, SciPy, and Polars is expected. Experience with digital signal processing, mobile sensor physics, or behavioural signal design. Proven track record of designing and delivering scalable data products, driving More ❯

Mathematics, or related fields. Proven experience in data science, advanced analytics, or quantitative analysis Strong programming skills in Python or R, with experience in data analysis libraries (pandas, scikit-learn) SQL proficiency for data querying and manipulation. Proficiency with data visualization tools (e.g., Microstrategy, Tableau) or dashboarding in Python (e.g., Plotly, Streamlit). Solid foundation in machine learning 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 ❯

collaborative, face-to-face, and iterative development environment. Skills & Experience Strong foundation in applied mathematics, statistics, or machine learning. Experience building and deploying models in Python (pandas, NumPy, scikit-learn, etc.). Comfort working with large and complex datasets, including data wrangling and exploratory analysis. Experience (or strong interest) in GPU-powered model development (e.g. using PyTorch, TensorFlow More ❯