1 to 25 of 52 Jupyter Jobs

that serve real users at scale without incurring technical debt. Proficiency in statistical methods and experience following CRISP-DM data science lifecycle. Expertise taking projects from ideation or experimental Jupyter notebooks to full production deployment. Strong programming skills in Python, with familiarity in ML libraries/frameworks such as TensorFlow, PyTorch, and Scikit-learn. Experience with MLOps practices including model More ❯

to Have (But Not Required): Experience from a university project, hackathon, or internship involving AI or machine learning Understanding of neural networks, NLP, or computer vision concepts Experience using Jupyter notebooks, Git, or cloud platforms (AWS, GCP, Azure) Knowledge of SQL or data wrangling techniques What You’ll Gain Real-World Experience : Work on live AI projects with real business More ❯

to Have (But Not Required): Experience from a university project, hackathon, or internship involving AI or machine learning Understanding of neural networks, NLP, or computer vision concepts Experience using Jupyter notebooks, Git, or cloud platforms (AWS, GCP, Azure) Knowledge of SQL or data wrangling techniques What You’ll Gain Real-World Experience : Work on live AI projects with real business More ❯

to Have (But Not Required): Experience from a university project, hackathon, or internship involving AI or machine learning Understanding of neural networks, NLP, or computer vision concepts Experience using Jupyter notebooks, Git, or cloud platforms (AWS, GCP, Azure) Knowledge of SQL or data wrangling techniques What You’ll Gain Real-World Experience : Work on live AI projects with real business More ❯

to Have (But Not Required): Experience from a university project, hackathon, or internship involving AI or machine learning Understanding of neural networks, NLP, or computer vision concepts Experience using Jupyter notebooks, Git, or cloud platforms (AWS, GCP, Azure) Knowledge of SQL or data wrangling techniques What You’ll Gain Real-World Experience : Work on live AI projects with real business More ❯

on complex multi-agent systems using frameworks like LangGraph, pushing the boundaries of applied AI in production. Full Ownership: Own the entire lifecycle, from research and design in a Jupyter Notebook to robust, automated deployment via CI/CD pipelines. Mission-Critical Impact: Work on solutions deployed in constrained, highly secure environments, delivering reliable performance where it matters most. Influence More ❯

on complex multi-agent systems using frameworks like LangGraph, pushing the boundaries of applied AI in production. Full Ownership: Own the entire lifecycle, from research and design in a Jupyter Notebook to robust, automated deployment via CI/CD pipelines. Mission-Critical Impact: Work on solutions deployed in constrained, highly secure environments, delivering reliable performance where it matters most. Influence 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 ❯

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 ❯

based AI pipelines, deploy them into cloud and edge environments, and own the infrastructure that keeps them secure, scalable, and cost-efficient. You will be as comfortable in a Jupyter Notebook as you are in a Terraform config, with the ability to turn research ideas into production systems that run reliably in constrained environments. Key Responsibilities AI & Agentic Development - Architect More ❯

based AI pipelines, deploy them into cloud and edge environments, and own the infrastructure that keeps them secure, scalable, and cost-efficient. You will be as comfortable in a Jupyter Notebook as you are in a Terraform config, with the ability to turn research ideas into production systems that run reliably in constrained environments. Key Responsibilities AI & Agentic Development - Architect More ❯

based AI pipelines, deploy them into cloud and edge environments, and own the infrastructure that keeps them secure, scalable, and cost-efficient. You will be as comfortable in a Jupyter Notebook as you are in a Terraform config, with the ability to turn research ideas into production systems that run reliably in constrained environments. Key Responsibilities AI & Agentic Development - Architect More ❯

based AI pipelines, deploy them into cloud and edge environments, and own the infrastructure that keeps them secure, scalable, and cost-efficient. You will be as comfortable in a Jupyter Notebook as you are in a Terraform config, with the ability to turn research ideas into production systems that run reliably in constrained environments. Key Responsibilities AI & Agentic Development - Architect More ❯

have : Experience with Python Experience with JavaFX Experience with JavaScript Experience with Bash Experience with Perl Experience with XML Experience with Eclipse Experience with Visual Studio Code Experience with Jupyter Notebook Experience with OSGi Experience with Google Guice Experience with Unit testing Experience with JUnit Experience with Automated Testing Experience with Squish Experience with Jenkins Experience with Dev Ops Experience More ❯

working as a Data Scientist or Machine Learning Engineer in a commercial setting. Strong programming skills in Python, with hands-on experience using libraries such as Pandas, Scikit-Learn, Jupyter, and Matplotlib. Proficient in SQL for data extraction and transformation. Experience with Google Cloud Platform (GCP) and Vertex AI for developing and deploying ML services is highly desirable. Solid understanding More ❯

best practices . Apply modern software engineering standards including testing, version control, and CI/CD pipelines. Key Skills: Proven experience in Python programming for data analysis (pandas, numpy, Jupyter). Strong object-oriented programming and design pattern knowledge. Experience with SQL and relational databases. Familiarity with data visualisation tools (e.g., Plotly Dash, Power BI). Understanding of software development More ❯

best practices . Apply modern software engineering standards including testing, version control, and CI/CD pipelines. Key Skills: Proven experience in Python programming for data analysis (pandas, numpy, Jupyter). Strong object-oriented programming and design pattern knowledge. Experience with SQL and relational databases. Familiarity with data visualisation tools (e.g., Plotly Dash, Power BI). Understanding of software development More ❯

best practices . Apply modern software engineering standards including testing, version control, and CI/CD pipelines. Key Skills: Proven experience in Python programming for data analysis (pandas, numpy, Jupyter). Strong object-oriented programming and design pattern knowledge. Experience with SQL and relational databases. Familiarity with data visualisation tools (e.g., Plotly Dash, Power BI). Understanding of software development More ❯

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, classification, clustering, time-series More ❯

brilliant opportunity to learn, grow, and contribute- you will: Support the team in sourcing, cleaning, and preparing data for analysis and modelling Learn to use data science tools (Python, Jupyter, cloud platforms) and apply them to real BBC projects Assist in running experiments and evaluations of machine learning models, guided by senior colleagues Contribute to simple visualisations and reports that More ❯

brilliant opportunity to learn, grow, and contribute – you will: Support the team in sourcing, cleaning, and preparing data for analysis and modelling. Learn to use data science tools (Python, Jupyter, cloud platforms) and apply them to real BBC projects. Assist in running experiments and evaluations of machine learning models, guided by senior colleagues. Contribute to simple visualisations and reports that 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 ❯

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 ❯

Python) Start-up/Scale-up Experience Strong cloud skills (AWS, GCP, Azure) and containerisation (Docker, Kubernetes) Experience in automating deployments and orchestrating cloud environments Nice to have: Python (Jupyter, PyTorch), monitoring tools (Prometheus, Grafana), cloud databases (RDS, Aurora, Spanner), CI/CD tools (CircleCI), and data visualisation experience. This is a unique opportunity to join a visionary team redefining More ❯