51 to 75 of 191 NumPy Jobs

t always work 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 More ❯

both a technical and non-technical audience Independent and self-driven learner, able to step outside of their area of expertise Python; we work with asyncio, SQLAlchemy, FastAPI, Pydantic, NumPy, Pandas SQL; performance tuning, schema design, monitoring in production, we mainly work with PostgreSQL Experience with transformer LLMs - attractive, nice to have Cloud (AWS) deployments and monitoring, basic networking and More ❯

instruction of established Quant Analytics team. What You Will Bring • University degree or equivalent with proven and displayed competency in data interrogation. • Strong proficiency and experience in Python (e.g., NumPy, pandas, scikit-learn) and quantitative thinking - you enjoy working with data to unearth patterns, trends, and nuances. • Exceptional analytical, problem-solving, and communication skills, with the ability to translate and More ❯

Airflow and dbt. Collaborate with stakeholders to deliver impactful solutions. Ensure data quality, security, and governance. About You Experience in analytics or model/data engineering. Advanced Python skills (Numpy/Pandas). Strong SQL and relational database design expertise. Excellent communication skills. Benefits £6,000 per annum training & conference budget to help you up-skill and elevate your career More ❯

Effectively manage technical priorities, meet deadlines, and deliver on project objectives. Masters degree in a STEM field (maths, science, engineering etc.) or equivalent Strong programming skills in Python (e.g., NumPy, Pandas, scikit-learn, TensorFlow/PyTorch). Demonstrable experience in creating and developing Python libraries. Demonstrable experience designing, implementing and training machine learning models from scratch. Strong foundations in applied More ❯

workflow orchestration tools (Airflow, Prefect, Temporal) or have built custom pipeline systems for multi-step autonomous processes. You bridge science and engineering. You are comfortable with scientific computing libraries (NumPy, SciPy, pandas) and understand scientific literature formats, databases (PubMed, arXiv), and academic data processing. What Sets You Apart: You have a research background. You are a former academic researcher who More ❯

specific challenges Drive the adoption of best practices in data science across the organisation, lead other data science engineers MINIMUM QUALIFICATIONS Industry experience using Python for data science (e.g. numpy, scipy, scikit, pandas, etc.) and SQL or other languages for relational databases. Experience with a cloud platform such as (AWS, GCP, Azure etc.) Experience with common data science tools; statistical More ❯

skills with experience building complex, maintainable systems Professional software development experience with a track record of delivering high-quality, production-grade code Experience with scientific computing libraries such as NumPy, Pandas, or SciPy in production environments Holistic software development mindset covering testing, documentation, security, and performance Track record of mentoring other engineers and sharing knowledge across teams Working knowledge of More ❯

skills with experience building complex, maintainable systems Professional software development experience with a track record of delivering high-quality, production-grade code Experience with scientific computing libraries such as NumPy, Pandas, or SciPy in production environments Holistic software development mindset covering testing, documentation, security, and performance Track record of mentoring other engineers and sharing knowledge across teams Desirable: Working knowledge More ❯

with Python and Django on an increasingly more service-oriented architecture. The rest of the tech stack includes Django REST Framework, PostgreSQL, AWS, React.js, Kubernetes, Docker, Redis, Celery, Pandas, Numpy, Git, Jenkins, and Elasticsearch. We have a very large but clean code base as we put significant emphasis on design patterns, code readability, automated testing, maintainability, and extendability. Responsibilities Work More ❯

React Router, MaterialUI, GitHub actions, on an increasingly more service-oriented architecture. The rest of the tech stack include Django REST Framework, PostgreSQL, AWS, Kubernetes, Docker, Redis, Celery, Pandas, Numpy, Github, Jenkins, Elasticsearch and lots of raw SQL for analytics. We have a very large but clean code base as we put significant emphasis on design patterns, code readability, testability More ❯

data engineering. Ability to work standard European time-zone hours and legal authorisation to work in your country of residence. Strong experience with Python's data ecosystem (e.g., Pandas, NumPy) and deep expertise in SQL for building robust data extraction, transformation, and analysis pipelines. Hands-on experience with big data processing frameworks such as Apache Spark, Databricks, or Snowflake, with More ❯

skills with experience building complex, maintainable systems Professional software development experience with a track record of delivering high-quality, production-grade code Experience with scientific computing libraries such as NumPy, Pandas, or SciPy in production environments Holistic software development mindset covering testing, documentation, security, and performance Track record of mentoring other engineers and sharing knowledge across teams Desirable: Working knowledge More ❯

with Python and Django on an increasingly more service-oriented architecture. The rest of the tech stack include Django REST Framework, PostgreSQL, AWS, React.js, Kubernetes, Docker, Redis, Celery, Pandas, Numpy, Git, Jenkins and Elasticsearch. We have a very large but clean code base as we put significant emphasis on design patterns, code readability, automated testing, maintainability and extendability. Responsibilities Work More ❯

C++, Python or related language - Experience with neural deep learning methods and machine learning PREFERRED QUALIFICATIONS - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. - Experience with large scale distributed systems such as Hadoop, Spark etc. Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status More ❯

a track record of handling high-visibility, customer-facing outputs. 1+ years experience using Python (or another programming language e.g. R, C++, Java) and with the scientific computing stack (Numpy, Pandas, SciPy, ScikitLearn, etc.) Familiarity with renewable energy technologies, market design, and regulatory frameworks within European power markets, specifically GB, Germany, Spain, Portugal, France, or Italy. Experience writing technical, report More ❯

a track record of handling high-visibility, customer-facing outputs. 1+ years experience using Python (or another programming language e.g. R, C++, Java) and with the scientific computing stack (Numpy, Pandas, SciPy, ScikitLearn, etc.) Familiarity with renewable energy technologies, market design, and regulatory frameworks within European power markets, specifically GB, Germany, Spain, Portugal, France, or Italy. Experience writing technical, report More ❯

Strong end-to-end delivery skills, including model deployment and operationalisation, with a solid grasp of MLOps best practices. Proficient in Python and key data science libraries such as NumPy, Pandas, Scikit-learn, PyTorch, and Statsmodels. Solid academic background in mathematics, computer science, or a related field, ideally with an MSc or PhD Practical experience with cloud platforms (preferably GCP More ❯

work. Data Engineering & Modelling: Understanding of ORM/entity relationships, NoSQL, JSON, XML, SQL and exposure to data visualisation tools (ETL/ELT). Data Science Tools: Proficiency in Numpy, Pandas, Matplotlib, Seaborn and Scikit-learn. MLOps: Experience building pipelines (CI/CD) using Bicep or similar technologies. Expertise in deploying, monitoring and managing machine learning models in production environments. More ❯

on experience with OpenAI APIs and Microsoft Azure AI services. Strong understanding of large language models, prompt engineering, and fine-tuning techniques. Experience with data manipulation libraries (e.g., Pandas, NumPy) and cloud platforms. Familiarity with MLOps, version control (e.g., Git), and deployment tools (e.g., Docker, Kubernetes). Excellent problem-solving skills and the ability to work on complex AI challenges. More ❯

structured and unstructured datasets of varying sizes. Track record of implementing impactful models that drive sustained business results. Proficiency in the Python data science tech stack (pandas, scikit-learn, NumPy, and visualisation libraries) Experience working in a Linux-based cloud environment (e.g. GCP, Azure, AWS). Experience using git version control. Communication, stakeholder management, and problem-solving skills are critical. More ❯

Research: Extensive hands-on experience using Python for data analysis, scientific computing, prototyping, and automation. You should be comfortable with core data science and signal processing libraries (e.g., Pandas, NumPy, SciPy, Scikit-learn, Librosa). Proven Research Background: A strong track record of performing high-quality, unsupervised research in a relevant field, with a portfolio of projects you have significantly More ❯

SQL, preferably with an enterprise database system like Redshift, Postgres. Ideally experience with data processing tools like DBT and Dask. Experience in Python data science space - LangChain, LangSmith, pandas, numpy, sci kit learn, scipy, hugging face etc. Understanding of statistical and machine learning models. Knowledge of experimental design, statistical testing and model validation. Experience in data visualization tools such as More ❯

designing and implementing efficient data pipelines, including performance tuning and optimization. Proven ability to apply machine learning techniques to real-world problems. Familiarity with core libraries such as Pandas, NumPy, Scikit-learn, SciPy, and Polars is expected. Experience with digital signal processing, mobile sensor physics, or behavioural signal design. Proven track record of designing and delivering scalable data products, driving More ❯

and varied opinions. Experience in a range of tools sets comparable with our own: Database technologies: SQL, Redshift, Postgres, DBT, Dask, airflow etc. AI Feature Development: LangChain, LangSmith, pandas, numpy, sci kit learn, scipy, hugging face, etc. Data visualization tools such as plotly, seaborn, streamlit etc You are Able to chart a path on a long term journey through much More ❯