16 of 16 Jupyter Jobs in London

experience with tree-based algorithms (XGBoost, LightGBM) and other ML methods Solid grasp of statistics, probability, and applied maths Proficiency in SQL , familiarity with NoSQL Comfortable using Git and Jupyter notebooks A practical, problem-solving mindset with a focus on clean, reliable delivery Nice to have: Experience with R or other statistical tools Exposure to model deployment/MLOps environments More ❯

experience with tree-based algorithms (XGBoost, LightGBM) and other ML methods Solid grasp of statistics, probability, and applied maths Proficiency in SQL , familiarity with NoSQL Comfortable using Git and Jupyter notebooks A practical, problem-solving mindset with a focus on clean, reliable delivery Nice to have: Experience with R or other statistical tools Exposure to model deployment/MLOps environments More ❯

understanding of strengths and weaknesses of Generative LLM's • Fundamental knowledge of ML, and basic knowledge of AI, NLP, and Large Language Models (LLM) • Comfortable working with Python and Jupyter Notebooks • Should have in-depth knowledge and familiarity with cloud platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Technical Skills – Good to have: • Expertise in More ❯

issues in live production environments. We're looking for individuals with: Experience: Proven background as a Machine Learning Engineer. Technical Skills: Strong in SQL and Python (Pandas, Scikit-learn, Jupyter, Matplotlib). Data transformation & manipulation : experience with Airflow, DBT and Kubeflow Cloud: Experience with GCP and Vertex AI (developing ML services). Expertise: Solid understanding of computer science fundamentals and More ❯

Collaborate with Research Teams – Translate experimental findings into production-grade systems that extend the autonomy and reliability of agents. Run Experiments End-to-End – Own your compute environment (e.g. Jupyter, Colab, Databricks) and iterate on large-scale LLM training and evaluation. What You’ll Bring: 4+ years’ experience in Machine Learning or AI , with exposure to LLM agent systems , tool More ❯

Collaborate with Research Teams – Translate experimental findings into production-grade systems that extend the autonomy and reliability of agents. Run Experiments End-to-End – Own your compute environment (e.g. Jupyter, Colab, Databricks) and iterate on large-scale LLM training and evaluation. What You’ll Bring: 4+ years’ experience in Machine Learning or AI , with exposure to LLM agent systems , tool More ❯

findings and recommendations to non-technical stakeholders 3. Ideal Qualifications 5+ years of applied data science or analytics experience in business settings Proficiency in Python or R (pandas, NumPy, Jupyter) and strong SQL skills Experience with data visualization tools (e.g., Tableau, Power BI) Solid understanding of statistical modeling, experimentation, and A/B testing Strong communication skills for translating technical More ❯

cutting-edge ML technology across the business. Machine Learning Engineer, key skills: Significant experience working as a Machine Learning Engineer Solid knowledge of SQLandPython's ecosystem for data analysis (Jupyter, Pandas, Scikit Learn, Matplotlib). GCP, VertexAI experience is desirable (developing GCP machine learning services) Timeseries forecasting Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling and More ❯

production. Requirements: Experience in a client-facing technical role (Solutions Engineer, AI Consultant, or similar) Strong Python skills and experience with LLMs/LangChain Confident building quick prototypes (Streamlit, Jupyter, etc.) Able to bridge business needs and technical delivery Startup mindset: fast-moving, collaborative, hands-on Bonus: experience in finance, legal, or insurance; Azure exposure; ISO 27001 familiarity This is More ❯

workflow management tools (Airflow/Argo) Prior experience writing documentation for senior stakeholders; the ability to accurately abstract and summarize technical information is critical Python programming skills: PySpark, Pandas, Jupyter Notebooks (3+ years in a professional environment) Prior experience working with git in a professional environment Ability to work independently in a fast-paced environment; prioritize multiple tasks and projects More ❯

Python data libraries (Pandas, NumPy) (preferably) from the Trading world. They also are on the look out for candidates who: Have deep familiarity with Python data ecosystem Understanding of Jupyter notebooks Exposure to machine learning libraries like PyTorch, XGBoost and JAX Understanding of crypto or traditional financial markets Strong API design and documentation skills What do you get in return More ❯

Python data libraries (Pandas, NumPy) (preferably) from the Trading world. They also are on the look out for candidates who: Have deep familiarity with Python data ecosystem Understanding of Jupyter notebooks Exposure to machine learning libraries like PyTorch, XGBoost and JAX Understanding of crypto or traditional financial markets Strong API design and documentation skills What do you get in return More ❯

required: 3+ years working as a Data Analyst, ideally within FinTech or Financial Services Exposure to Derivatives or other Financial instruments Strong SQL experience Strong Python experience (PySpark, Pandas, Jupyter Notebooks etc.) Airflow/Algo/Alteryx for workflow management Git Any exposure to compressed file formats such as Parquet or HDF5 highly advantageous Linux/Bash skills highly desirable More ❯

required: 3+ years working as a Data Analyst, ideally within FinTech or Financial Services Exposure to Derivatives or other Financial instruments Strong SQL experience Strong Python experience (PySpark, Pandas, Jupyter Notebooks etc.) Airflow/Algo/Alteryx for workflow management Git Any exposure to compressed file formats such as Parquet or HDF5 highly advantageous Linux/Bash skills highly desirable More ❯

as designing experiments, gathering and preprocessing data, building and evaluating models, and collaborating closely with engineering teams to deploy production‐ready solutions. Ideal candidates should be proficient in Python (Jupyter Notebooks), familiar with machine learning frameworks like TensorFlow or PyTorch, and experienced in analyzing large datasets and building predictive models. In addition, you will write, review, and validate prompt‐based … train AI systems. You are a good fit if you: Have over 3+ years of professional experience in data science or applied analytics. Are highly skilled in Python and Jupyter notebooks. Have experience using libraries including numpy, pandas, scipy, sympy, scikit-learn, torch, tensorflow. Have a bachelor's degree in data science, statistics, computer science, or related field in the More ❯

research/production use. Scale up and productionize research model pipelines into large-scale, reliable distributed compute jobs. Manage distributed compute workflows with Dask, Ray, and other frameworks. Develop Jupyter tooling, templates, and widgets for strategy prototyping and parameter tuning. Implement performance attribution, PnL decomposition, and diagnostic tools. Build dashboards and visualizations (Bokeh, Matplotlib) for order book and strategy monitoring. … development experience. Extremely effective in writing clean, modern Python code. Experience building and documenting high-quality APIs for developers. Deep Python ecosystem expertise: Pandas, Numpy, Bokeh, PyArrow, matplotlib, IPyWidgets, Jupyter, uv, etc. Proven experience building large data pipelines/ETL workflows. Comfortable working in Jupyter notebooks. An understanding of crypto or TradFi markets and trading concepts. Nice to Have Experience More ❯