1 to 25 of 52 scikit-learn Jobs in London

language) and in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries ...

automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation ...

GenAI: Experience with LLM APIs and Context Engineering (constructing prompts, managing context windows, evaluating behaviour). Engineering & Stack: Python: Expert level (Pandas, NumPy, Scikit-Learn). Data Engineering: Strong SQL skills and experience building data pipelines. Experience: Education: Degree in a quantitative field (Statistics, Mathematics, Computer Science, etc. ...

patterns, demand profiles, and commercial optimisation. Required Skills and Experience Technical Skills Proficiency in Python and associated data science libraries (NumPy, Pandas, SciPy, scikit‐learn, PyTorch/TensorFlow). Strong experience with time‐series modelling (ARIMA, Prophet, LSTMs or similar). Understanding of optimisation methods (linear, mixed‐integer ...

technical stakeholders What we're looking for Essential experience Strong grounding in statistical modelling, experimentation and inference Advanced Python skills (NumPy, pandas, scikit-learn, PyTorch or TensorFlow) Experience building, deploying and optimising ML models in production Strong AWS experience (e.g. SageMaker, Lambda or similar services) Expert SQL skills ...

Python and its core data science libraries: Pandas, NumPy, and SciPy . Machine Learning: Deep, practical experience with popular ML frameworks and libraries: scikit-learn, TensorFlow, or PyTorch . Statistics & Math: Strong foundation in statistical modeling , probability, hypothesis testing, regression analysis, and multivariate calculus/linear algebra ...

into reliable, scalable systems. Essential experience includes: Commercial experience developing and deploying machine learning models in Python Proficiency with ML frameworks such as scikit-learn, XGBoost, PyTorch or TensorFlow Strong experience delivering ML solutions using AWS services (e.g. SageMaker, Lambda, S3) Expertise in experiment design, including hypothesis formulation ...

such as NLP, computer vision, audio, graphs, time-series or tabular data Strong Python skills and experience with common libraries (e.g. pandas, NumPy, scikit-learn, PyTorch, Transformers, statsmodels, PyMC) Understanding of software engineering and ML best practices, including version control, packaging and model deployment Experience training and deploying ...

years' experience as a data scientist in industry or related environments. Proficiency in Python and common ML/data libraries (e.g., pandas, numpy, scikit-learn, PyTorch, transformers). Understanding of software and ML engineering best practices (git, package development, model deployment). Experience with cloud platforms, particularly ...

junior colleagues and share learnings across the team. Requirements: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow. Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments. Strong experiment design skills: hypothesis formulation ...

data scientist in industry or similar environment. Good knowledge of python, along with common machine learning and data analysis libraries (e.g. pandas, numpy, scikit-learn, pytorch, transformers, statsmodels, pymc). Ability to absorb complex new scientific concepts and quickly get up to speed on new areas ...

will have experience in many of the following: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/ ...

systems, personalisation, or deep learning is desirable but not essential. Solid Python skills and experience with ML libraries such as pandas, numpy, scipy, scikit-learn, TensorFlow or PyTorch. Experience working across cloud environments (GCP, AWS, or Azure) and analytics platforms such as Dataiku. Good understanding of MLOps practices ...

Statistics, or related field Strong programming skills in Python (and optionally R, Java, or C++) Knowledge of ML frameworks like TensorFlow, PyTorch, or scikit-learn Eagerness to learn, curiosity, and a problem-solving mindset Nice to Have Cloud experience (AWS, GCP, Azure) Familiarity with MLOps tools (Docker ...

solutions Implement experiment tracking and monitoring Collaborate with data scientists and engineers What We're Looking For: Proficiency in Python and ML frameworks (scikit-learn, PyTorch, TensorFlow) Hands-on experience with AWS ML services (SageMaker, Lambda) Familiarity with containerisation (Docker) and orchestration (Kubernetes/ECS) Knowledge of LLMOps ...

junior data scientists and help shape AI best practice Requirements Strong experience in AI, machine learning and statistics Python expertise (e.g. pandas, NumPy, scikit-learn, PyTorch, transformers) Experience deploying models and working with cloud platforms (AWS preferred) Ability to communicate complex technical concepts clearly Desirable AWS services (SageMaker ...

will have experience in many of the following: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/ ...

science practices tooling governance Essential Skills for the Data Scientist: Strong experience in data science using Python with libraries such as Pandas NumPy scikit-learn or similar Solid grounding in statistics probability machine learning techniques Proven experience working within cloud environments such as AWS Azure or GCP Experience ...

years' experience in ML/AI engineering or data science roles (senior level). Hands-on experience with Python ML frameworks (scikit-learn, PyTorch, TensorFlow, XGBoost). Experience with AWS ML services (SageMaker, Lambda, S3) and MLOps/LLMOps tooling (MLflow, Weights & Biases, DVC). Proven track record ...

engineers to productionise models Communicate insights to technical and non-technical stakeholders Mentor junior team members Skillset: Strong Python and ML libraries (scikit-learn, PyTorch, pandas, NumPy) Experience with AWS cloud services (SageMaker, Lambda, S3) Familiarity with MLOps tools (MLflow, Weights & Biases) Knowledge of NLP, computer vision ...

stakeholders Mentor junior engineers and contribute to team best practices This role is for you if you have: Strong Python ML experience (e.g. scikit-learn, XGBoost, PyTorch, or TensorFlow) Production experience with AWS ML services (e.g. SageMaker, Lambda, S3) Proven ability to move models from experimentation into live ...

SageMaker, Lambda, Redshift). Proven ability to design and implement machine learning algorithms and models. Proficiency in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow). Strong data analysis, statistical modelling, and experimentation skills. Experience with data visualisation tools and techniques. Proficiency in programming languages ...

experience delivering AI/ML projects end-to-end in production environments Strong Python skills with hands-on use of ML libraries like Scikit-learn, TensorFlow, PyTorch, or Hugging Face Solid understanding of machine learning fundamentals and performance evaluation techniques Experience working in cloud platforms ...

Fundamentals: You aren't limited to APIs. You have a background in training and evaluating models (TensorFlow/PyTorch/Keras/Scikit-learn) and know when a simple classifier is better than a generative model. Data Fluency: You understand how to build datasets from scratch (bootstrapping, crowdsourcing ...

conversational AI, recommendation systems, optimisation, or marketing analytics. Deep understanding of ML fundamentals across the full lifecycle. Hands‐on experience with ML frameworks (scikit‐learn, XGBoost, PyTorch, TensorFlow, LightGBM). Strong exposure to LLMs, prompt engineering, RAG, and evaluation frameworks. Familiarity with MLOps tooling: CI/CD, experiment ...