26 to 50 of 92 scikit-learn Jobs in London

setting. Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written 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 ❯

NumPy). Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions, including Snowflake. More ❯

through technical transformation. To be successful as a Lead Data Scientist, you should have experience with: Advanced Python Programming- Expert knowledge of data science libraries e.g. NumPy, Pandas, scikit-learn, PyTorch Machine Learning Expertise- Demonstrated experience in designing, training, evaluating, and deploying production-grade ML models Software Engineering Excellence- Experience building modular, maintainable code with CI/CD More ❯

or other relevant AI fields. Proven track record of designing, developing, and deploying AI systems in production environments. Proficient in Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices More ❯

or other relevant AI fields. Proven track record of designing, developing, and deploying AI systems in production environments. Proficient in Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices More ❯

or other relevant AI fields. Proven track record of designing, developing, and deploying AI systems in production environments. Proficient in Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices More ❯

or other relevant AI fields. Proven track record of designing, developing, and deploying AI systems in production environments. Proficient in Python and key ML libraries (e.g. PyTorch, PySpark, scikit-learn, Hugging Face Transformers). Hands-on experience with modern data platforms and AI tooling such as Azure ML, Databricks, MLflow, LangChain, LangGraph. Proven experience with modern engineering practices More ❯

reasonable industry experience, or an MS with significant industry or research experience in the field • Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas) • Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals • Experience with big data More ❯

pragmatic, and collaborative team player. Experience as a lead developer tackling complex problems at scale. Experience mentoring junior engineers. Familiarity with various machine learning frameworks and toolkits (e.g., scikit-learn, XGBoost, TensorFlow). Hands-on experience building GenAI solutions using patterns such as Retrieval-Augmented Generation (RAG) or fine-tuning Large Language Models (LLMs). Have experience with More ❯

background will include: 3+ years industry experience in a Data Science role and a strong academic background Python Data Science Stack: Advanced proficiency in Python , including pandas , NumPy , scikit-learn , and Jupyter Notebooks . Statistical & ML Modelling: Strong foundation in statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression More ❯

graph analytics and hands-on experience and solid understanding of machine learning and deep learning methodsExtensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goalsExperience with big data and More ❯

setting. Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written More ❯

setting. Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written More ❯

setting. Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written More ❯

setting. Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. Ability to explain model behaviour through visualisations and reports. Familiarity with Git and collaborative development workflows. Good written More ❯

last 10 years. You must be able to hold or gain a UK government security clearance. Preferred technical and professional experience Experience with machine learning frameworks (TensorFlow, PyTorch, scikit-learn). Familiarity with big data technologies (Hadoop, Spark). Background in data science, IT consulting, or a related field. AWS Certified Big Data or equivalent. IBM is committed More ❯

including experience with software testing (unit, integration, system), and knowledge of test-driven development; other languages are a plus. Proficiency in at least one ML framework, such as scikit-learn, XGBoost, Tensorflow, or PyTorch. Proficiency with Cloud platform(s), such as Google Cloud Platform, Amazon Web Services, or Azure. Experience in designing, and deploying ML pipelines in production More ❯

in Python, including solid experience with software testing (unit, integration, system), and knowledge of test-driven development; other languages are a plus. Proficiency in ML frameworks, such as scikit-learn, XGBoost, Tensorflow, or PyTorch. Proficiency with Cloud platform(s), such as Google Cloud Platform, Amazon Web Services, or Azure. Experience in designing, and deploying ML pipelines in production More ❯

grow and shape the data science team and its role within the wider business. What We’re Looking For Strong technical foundation with proficiency in Python (Pandas, NumPy, Scikit-learn), SQL, and cloud platforms (GCP or AWS). Experience with modern data warehouses (BigQuery, Snowflake, Redshift). Proven experience in deploying machine learning models or optimisation algorithms into More ❯

grow and shape the data science team and its role within the wider business. What We’re Looking For Strong technical foundation with proficiency in Python (Pandas, NumPy, Scikit-learn), SQL, and cloud platforms (GCP or AWS). Experience with modern data warehouses (BigQuery, Snowflake, Redshift). Proven experience in deploying machine learning models or optimisation algorithms into More ❯

Experience building interactive dashboards and reporting tools. • Familiarity with data engineering, ETL processes, and data pipelines. • Experience with statistical modelling and machine learning techniques, and libraries such as scikit-learn, PyTorch, or TensorFlow. • Excellent communication skills able to explain complex analysis to non-technical stakeholders. • Proactive, inquisitive mindset with strong problem-solving skills. • Interest in cyber risk and More ❯

Experience building interactive dashboards and reporting tools. • Familiarity with data engineering, ETL processes, and data pipelines. • Experience with statistical modelling and machine learning techniques, and libraries such as scikit-learn, PyTorch, or TensorFlow. • Excellent communication skills—able to explain complex analysis to non-technical stakeholders. • Proactive, inquisitive mindset with strong problem-solving skills. • Interest in cyber risk and More ❯

in artificial intelligence and its application to drug discovery Understanding of ligand- and structure-based drug design. Experience with machine learning and major machine learning frameworks such as scikit-learn or PyTorch Proficiency in a low-level programming language such as C/C++ or Rust. Knowledge of advanced data analysis techniques such as multi-parameter optimization Open More ❯