knowledge of React (not a frontend role, but an understanding of the stack is important) Hands-on experience with containerisation (Docker) and cloud deployment (Terraform, microservices, Azure) Exposure to Jupyter notebooks , and understanding of how machine learning models are developed and deployed Experience in fast-paced or start-up environments where you’ve contributed across the stack Background & Education Degree More ❯
knowledge of React (not a frontend role, but an understanding of the stack is important) Hands-on experience with containerisation (Docker) and cloud deployment (Terraform, microservices, Azure) Exposure to Jupyter notebooks , and understanding of how machine learning models are developed and deployed Experience in fast-paced or start-up environments where you’ve contributed across the stack Background & Education Degree More ❯
knowledge of React (not a frontend role, but an understanding of the stack is important) Hands-on experience with containerisation (Docker) and cloud deployment (Terraform, microservices, Azure) Exposure to Jupyter notebooks , and understanding of how machine learning models are developed and deployed Experience in fast-paced or start-up environments where you’ve contributed across the stack Background & Education Degree More ❯
london (city of london), south east england, united kingdom
Mentmore
knowledge of React (not a frontend role, but an understanding of the stack is important) Hands-on experience with containerisation (Docker) and cloud deployment (Terraform, microservices, Azure) Exposure to Jupyter notebooks , and understanding of how machine learning models are developed and deployed Experience in fast-paced or start-up environments where you’ve contributed across the stack Background & Education Degree More ❯
learning models in production environments. API Development: An understanding of REST. Experience with Flask or FastAPI. Data Validation: Knowledge of Pydantic for data validation. Scripting and Prototyping: Use of Jupyter Notebooks for quick prototyping. DevSecOps Practices: Understanding of secure coding and automated testing. Experience with Pytest or a Python testing framework. You'll be able to be yourself; we'll More ❯
field. Proven experience in machine learning applications such as recommendations, segmentation, forecasting, and marketing spend optimisation. Proficiency in Python, SQL, and Git, with hands-on experience in tools like Jupyter notebooks, Pandas, and PyTorch. Expertise in cloud platforms (AWS, Databricks, Snowflake) and containerisation tools (Docker, Kubernetes). Strong leadership skills with experience mentoring and managing data science teams. Deep knowledge More ❯
field. Proven experience in machine learning applications such as recommendations, segmentation, forecasting, and marketing spend optimisation. Proficiency in Python, SQL, and Git, with hands-on experience in tools like Jupyter notebooks, Pandas, and PyTorch. Expertise in cloud platforms (AWS, Databricks, Snowflake) and containerisation tools (Docker, Kubernetes). Strong leadership skills with experience mentoring and managing data science teams. Deep knowledge More ❯
Services (S3, EKS, ECR, EMR, etc.) •Experience with containers and orchestration (e.g. Docker, Kubernetes) •Experience with Big Data processing technologies (Spark, Hadoop, Flink etc) •Experience with interactive notebooks (e.g. JupyterHub, Databricks) •Experience with Git Ops style automation •Experience with ix (e.g, Linux, BSD, etc.) tooling and scripting •Participated in projects that are based on data science methodologies, and/or More ❯
AI/ML/Data Science apprenticeship programme. Core Skills & Competencies Technical Skills Programming proficiency in Python and common ML libraries such as TensorFlow, PyTorch, or similar. Experience with Jupyter Notebooks and version control (Git/GitHub). Basic understanding of supervised/unsupervised learning, neural networks, or clustering. Analytical Abilities Ability to interpret data trends, visualize outputs, and debug More ❯
data analysis. Strong technical skills regarding data analysis, statistics, and programming. Strong working knowledge of, Python, Hadoop, SQL, and/or R. Working knowledge of Python data tools (e.g. Jupyter, Pandas, Scikit-Learn, Matplotlib). Ability to talk the language of statistics, finance, and economics a plus. Profound knowledge of the English language. In a changing world, diversity and inclusion More ❯
Central London, London, United Kingdom Hybrid / WFH Options
Singular Recruitment
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 ❯
and evaluate highly innovative models for Natural Language Programming (NLP), Large Language Model (LLM), or Large Computer Vision Models. Use SQL to query and analyze the data. Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models. Use machine learning and analytical techniques to create scalable solutions for business problems. Research and implement novel machine learning More ❯
Comfortable working with imperfect data, ambiguity, and evolving priorities. Bonus: experience with DBT, cloud data warehouses (e.g. BigQuery), or automated experimentation platforms. Technology Python (incl. pandas, statsmodels, scikit-learn), Jupyter dbt, SQL (BigQuery, PostgreSQL) Tableau or similar BI tools GitHub, GCP, Docker (optional but useful) How we expect you to work ️ Collaboration : We work in cross-functional, autonomous squads where More ❯
and other Qualtrics products Acquire data from customers (usually sftp or cloud storage APIs) Validate data with exceptional detail orientation (including audio data) Perform data transformations (using Python and Jupyter Notebooks) Load the data via APIs or pre-built Discover connectors Advise our Sales Engineers and customers as needed on the data, integrations, architecture, best practices, etc. Build new AWS More ❯
on our data so you will need to understand how to develop your own models • Strong programming skills and experience working with Python, Scikit-Learn, SciPy, NumPy, Pandas and Jupyter Notebooks is desirable. Experience with object-oriented programming is beneficial • Publications at top conferences, such as NeurIPS, ICML or ICLR, is highly desirable Why should you apply? • Highly competitive compensation More ❯
revolution. The Solutions Engineering Team works closely with Bloomberg clients to assist them to implement quantitative investment strategies and research using our new Python Quant development platform. Powered by JupyterLab, the quant platform combines world-class open source Python libraries with the world's leading financial database, allowing our clients to generate unique research in quantitative finance and help them More ❯
revolution. The Solutions Engineering Team works closely with Bloomberg clients to assist them to implement quantitative investment strategies and research using our new Python Quant development platform. Powered by JupyterLab, the quant platform combines world-class open source Python libraries with the world's leading financial database, allowing our clients to generate unique research in quantitative finance and help them More ❯
and experience in GA4, Google Search Console, Google Tag Manager, Looker Studio, Google Cloud Console (Big Query), Google Apps Scripts Strong working knowledge of HTML, basic JavaScript, Python and Jupyter Notebooks as they relate to technical SEO analysis Proficiency in SEO audit tools such as SEMrush, Ahrefs, Screaming Frog, DeepCrawl, or similar Proficiency gathering marketing insights for analysis and reporting More ❯
UX development Communicate clearly and manage blockers proactively Your Profile: 1+ year professional or internship engineering experience Solid foundation in software design patterns and data structures Familiar with Git, Jupyter, command line, and agile workflows Experience with: React.js Node Python CSS Typescript Unit Testing AI/ML: LangChain, PyTorch, TensorFlow (basic understanding) Bonus: Interest in ethical AI, UX design, and More ❯
enrich data from various sources. Ensure that pipelines are automated, scalable, and fault-tolerant to accommodate large volumes of data. Experience with Notebooks, Pipelines, and Workflows: Utilise Notebooks (e.g., Jupyter, Databricks) for data exploration, analysis, and reporting. Design and optimise data workflows to streamline key processing tasks, enhancing operational efficiency. API Integration & Data Ingestion: Integrate external and internal APIs to More ❯
written communication skills, with the ability to explain data findings to both technical and non-technical audiences. Experience delivering data driven insights to businesses. Familiarity with tools such as Jupyter Notebook and basic Python for data analysis. Some exposure to cloud platforms (e.g., AWS, GCP, or Azure) and interest in learning cloud-based data tools. Experience in using BI tools More ❯
London, South East, England, United Kingdom Hybrid / WFH Options
Sanderson
technology across the business. Machine Learning Engineer, key skills: Significant experience working as a Data Scientist/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 ❯
as part of a cross functional team Preferred Qualifications Experience with Big Data technologies (e.g. HDFS, AWS, Spark, Kafka, Cassandra) Experience with Big Data query tools and engine (e.g. Jupyter Notebook, Trino, DBeaver) Experience with near real-time (NRT) and Batch data pipelines Experience black box testing Experience Client-Server products Knowledge in Data Quality, Data Profiling and Data Integration More ❯
and dedicated time for your personal development What you'll be working with: •Backend: Distributed, event-driven core Java (90% of the code-base), MySQL, Kafka •Data analytics: Python & Jupyter notebooks, Parquet, Docker •Testing: JUnit, JMH, JCStress, Jenkins, Selenium, many in-house tools •OS: Linux (Fedora for development, Rocky in production) The LMAX way is to use the right tool More ❯
