126 to 150 of 680 NumPy Jobs

of best practices in data science across the organisation, mentor 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 More ❯

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 More ❯

understanding of best practices in software engineering and data engineering Practical object-oriented programming experience in Python with knowledge of relevant packages including Pandas, NumPy, SciPy, Matplotlib, Scikit-learn, Pytorch In-depth knowledge of statistical and machine learning models as well as experience with end-to-end delivery lifecycles Experience More ❯

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 More ❯

regulations (GDPR) and implement bias mitigation. Employ model explainability methods (SHAP, LIME) and adopt best practices for responsible AI. Qualifications Technical Skills Programming: Python (NumPy, Pandas), R, SQL. ML/DL Frameworks: Scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers. Big Data & Cloud: Databricks, Azure ML, AWS SageMaker, GCP Vertex AI. More ❯

Markets, Particularly Oil, Gas, and Commodities. Business Analysis Skills including requirements gathering, process mapping, and data analysis. Experience with SQL, C#.Net, Git, Python (Panda, Numpy, ML libraries), and data visualization tools such as Power BI or Tableau. Benefits & perks Competitive salary Vitality health insurance and dental cover 38 days of More ❯

architect high-quality software solutions. Develop IT solutions using Python and related libraries for AI applications. Build scalable data pipelines utilizing technologies like Pandas, NumPy, or Spark. Apply Agile and DevOps practices to deliver effective solutions. Understand and translate business requirements into technical specifications. Identify opportunities for enhancing DevOps processes More ❯

development experience using languages such as Python, C, Julia, C++, or similar. • Strong proficiency in Python and its scientific computing/ML ecosystem (e.g., NumPy, Pandas, TensorFlow, AI Foundry, OpenAI, Scikit-learn etc.). • Solid understanding of core machine learning concepts • Hands-on experience with at least one major deep More ❯

communication skills Nice To Have: Prior exposure with execution algos, TCA, order-routing, or market-impact modelling Knowledge of statistical or machine-learning libraries (NumPy, pandas, scikit-learn, PyTorch) Experience building distributed systems with message buses (Kafka, ZeroMQ) and asynchronous I/O Experience with cloud or on-prem orchestration More ❯

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 More ❯

Data Science, Computer Science, Statistics, Mathematics, or related fields (PhD a plus). Strong programming skills in Python, with proficiency in data science libraries (NumPy, Pandas, scikit-learn). Experience with SQL for data querying and analysis. Solid understanding of machine learning algorithms, statistical methods, and predictive modeling. Experience with More ❯

Experience with Python A keen interest in backend development Understanding of distributed systems (RabbitMQ, Celery, Redis, Memcached) Good knowledge of scientific libraries such as Numpy and Pandas Extensive usage of MongoDB, BigQuery, data visualisation and manipulation tools Experience with designing, training, evaluating machine learning models Familiarity with the main AI More ❯

Experience with Python A keen interest in backend development Understanding of distributed systems (RabbitMQ, Celery, Redis, Memcached) Good knowledge of scientific libraries such as Numpy and Pandas Extensive usage of MongoDB, BigQuery, data visualisation and manipulation tools Experience with designing, training, evaluating machine learning models Familiarity with the main AI More ❯

R, Java, C++). Machine Learning & AI Frameworks: Experience with TensorFlow, PyTorch, Scikit-learn, or similar libraries. Data Manipulation & Analysis: Strong skills in Pandas, NumPy, and SQL for handling and analysing large datasets. Cloud Computing: Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for AI model deployment. More ❯

/B testing, entity extraction, and feature engineering. Proficiency in programming languages such as Python, R, and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, NLTK). Experience with NLP techniques, such as named More ❯

R, Java, C++). Machine Learning & AI Frameworks: Experience with TensorFlow, PyTorch, Scikit-learn, or similar libraries. Data Manipulation & Analysis: Strong skills in Pandas, NumPy, and SQL for handling and analysing large datasets. Cloud Computing: Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for AI model deployment. More ❯

/B testing, entity extraction, and feature engineering. Proficiency in programming languages such as Python, R, and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, NLTK). Experience with NLP techniques, such as named More ❯

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 More ❯

time series, spatial, and network data. Excellent programming skills in common languages (e.g., Python) and packages used by the energy modeling field (e.g., geopandas, numpy, networkx, pandas), use of software best practices (e.g., Git), and familiarity with high-performance computing environments. Experience with extracting, transforming, and loading processes and tools More ❯

applications for Windows desktops or embedded systems. Experience with privacy, security, or qualifications in machine learning. Experience using PyTorch, TensorFlow for machine learning, or NumPy for general data science tasks. The interview process: Getting to know you: 30 minute chat with our People and Operations manager to talk about your More ❯

time series, spatial, and network data. Excellent programming skills in common languages (e.g., Python) and packages used by the energy modeling field (e.g., geopandas, numpy, networkx, pandas), use of software best practices (e.g., Git), and familiarity with high-performance computing environments. Experience with extracting, transforming, and loading processes and tools More ❯

for solving problems, ideally including Large Language Models Proficiency in Python, particularly with machine learning and data science libraries such as PyTorch, scikit-learn, numpy and scipy Good communication and interpersonal skills Experience working with large-scale data processing systems such as Spark and Hadoop Experience in software development in More ❯

experience with Python libraries like Flask, FastAPI, Pandas, PySpark, PyTorch, to name a few. Proficiency in statistics and/or machine learning libraries like NumPy, matplotlib, seaborn, scikit-learn, etc. Experience in building ETL/ELT processes and data pipelines with platforms like Airflow, Dagster, or Luigi. What's important More ❯

Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn. Proficiency in data manipulation and analysis using tools such as Polars, Pandas, NumPy, or SQL. Solid understanding of algorithms, statistics, and data structures. Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). Knowledge of CI More ❯

Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn. Proficiency in data manipulation and analysis using tools such as Polars, Pandas, NumPy, or SQL. Solid understanding of algorithms, statistics, and data structures. Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes). Knowledge of CI More ❯