51 to 75 of 237 NumPy Jobs

up Having added nearly 100 people to the business this year, they're looking for Quantitative Developers with proficiency in Python, and experience with the 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 More ❯

required for this role. Essential Skills & Experience: • Proven experience developing solutions with Power Apps (Canvas and Model-driven apps). • Strong proficiency in Python, including libraries such as Pandas, NumPy, and Flask or FastAPI. • Experience with Microsoft Power Platform, including Power Automate and Dataverse. • Ability to mentor and support junior staff in technical development. • Excellent communication and stakeholder engagement skills. More ❯

learning, particularly in recommendation systems and deep learning architectures Strong understanding of two-tower neural networks, embedding techniques, and ranking models Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku Experience with ML Ops, including deployment, monitoring, and retraining pipelines Ability to work cross More ❯

learning, particularly in recommendation systems and deep learning architectures Strong understanding of two-tower neural networks, embedding techniques, and ranking models Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku Experience with ML Ops, including deployment, monitoring, and retraining pipelines Ability to work cross More ❯

learning, particularly in recommendation systems and deep learning architectures Strong understanding of two-tower neural networks, embedding techniques, and ranking models Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku Experience with ML Ops, including deployment, monitoring, and retraining pipelines Ability to work cross More ❯

optimisation Analytical mindset with interest in systematic trading and quantitative research Nice to Have Exposure to trading systems, market data, or algo/bot development Familiarity with numerical libraries (NumPy, Pandas, Numba, etc.) Understanding of version control, CI/CD, or distributed computing More ❯

optimisation Analytical mindset with interest in systematic trading and quantitative research Nice to Have Exposure to trading systems, market data, or algo/bot development Familiarity with numerical libraries (NumPy, Pandas, Numba, etc.) Understanding of version control, CI/CD, or distributed computing More ❯

job offered or in any occupation in related field. Special Skill Requirements: (1) Java; (2) Python; (3) React; (4) Node.js; (5) Git; (6) Jenkins; (7) Spark; (8) SQL; (9) Numpy; (10) AWS and (11) Docker. Any suitable combination of education, training and/or experience is acceptable. Telecommuting is permitted. Salary: $194,250.00 - $244,872.00 per annum and standard company More ❯

deep learning, reinforcement learning, generative models). Support continuous model improvement and scalable MLOps deployment pipelines. TECH STACK/REQUIREMENTS Core Skills: Python, TensorFlow/PyTorch, scikit-learn, OpenCV, NumPy, Pandas Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes More ❯

deep learning, reinforcement learning, generative models). Support continuous model improvement and scalable MLOps deployment pipelines. TECH STACK/REQUIREMENTS Core Skills: Python, TensorFlow/PyTorch, scikit-learn, OpenCV, NumPy, Pandas Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes More ❯

deep learning, reinforcement learning, generative models). Support continuous model improvement and scalable MLOps deployment pipelines. TECH STACK/REQUIREMENTS Core Skills: Python, TensorFlow/PyTorch, scikit-learn, OpenCV, NumPy, Pandas Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes More ❯

experience, ideally in Treasury or from a hedge fund/buy-side firm Expertise in Python for designing and optimising complex applications Strong proficiency in data tools like Pandas, NumPy, SQL, and real-time data processing Familiarity with DevOps practices, including Linux, cloud platforms, and CI/CD (Docker/Kubernetes is desirable but not essential). Strong communication skills More ❯

experience, ideally in Treasury or from a hedge fund/buy-side firm Expertise in Python for designing and optimising complex applications Strong proficiency in data tools like Pandas, NumPy, SQL, and real-time data processing Familiarity with DevOps practices, including Linux, cloud platforms, and CI/CD (Docker/Kubernetes is desirable but not essential). Strong communication skills More ❯

machine learning Familiarity with supervised and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms (GCP, AWS, Azure) and tools like Dataiku and Databricks Experience with ML Ops, including deployment, monitoring, and retraining pipelines Ability to More ❯

months with possible extensions (No Sponsorship Available ) Skills/Qualifications: · 4+ years of experience developing data pipelines and data warehousing solutions using Python and libraries such as Pandas, NumPy, PySpark, etc. · 3+ years hands-on experience with cloud services, especially Databricks, for building and managing scalable data pipelines · 3+ years of proficiency in working with Snowflake or similar cloud-based More ❯

libraries (Matplotlib, Seaborn.) SQL for data extraction and manipulation. Experience working with large datasets. Technical Skills Proficiency in cloud computing and python programming. Familiarity with Python libraries like Pandas, NumPy, scikit-learn. Experience with cloud services for mode training and deployment. Machine Learning Fundamentals Statistical concepts for robust data analysis. Linear algebra principles for modelling and optimisation. Calculus for optimising More ❯

libraries (Matplotlib, Seaborn.) SQL for data extraction and manipulation. Experience working with large datasets. Technical Skills Proficiency in cloud computing and python programming. Familiarity with Python libraries like Pandas, NumPy, scikit-learn. Experience with cloud services for mode training and deployment. Machine Learning Fundamentals Statistical concepts for robust data analysis. Linear algebra principles for modelling and optimisation. Calculus for optimising More ❯

in recommendation systems and deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch. Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work More ❯

constraints. Automate Data Pipelines, designing and managing workflows for collecting, cleaning, and storing large volumes of financial data (e.g., price, volume, fundamentals, alternative data), often using tools like Pandas, NumPy, and Dask. Collaborate Across Teams for Deployment, working with researchers, traders, and DevOps teams to integrate Python models into production environments (e.g., through APIs, microservices, or containerized systems like Docker More ❯

related pricing/trading systems Solid understanding of algorithms, data structures, and design patterns Experience with relational and NoSQL databases (PostgreSQL, Redis, MongoDB) Proficiency in data processing using pandas, NumPy , and working with APIs Understanding of CI/CD pipelines and DevOps practices Preferred: Quantitative analysis, financial modeling, or experience with trading systems Familiarity with distributed systems and event-driven More ❯

related pricing/trading systems Solid understanding of algorithms, data structures, and design patterns Experience with relational and NoSQL databases (PostgreSQL, Redis, MongoDB) Proficiency in data processing using pandas, NumPy , and working with APIs Understanding of CI/CD pipelines and DevOps practices Preferred: Quantitative analysis, financial modeling, or experience with trading systems Familiarity with distributed systems and event-driven More ❯

and version control processes. Stay up to date with advances in quantitative finance, computational techniques, and emerging technologies. Profile Strong programming experience in Python, C++, or C#; knowledge of NumPy, Pandas, and QuantLib advantageous. Solid understanding of mathematics, statistics, and numerical methods - including stochastic calculus, Monte Carlo simulation, and optimisation. Familiarity with derivatives pricing, risk metrics, and financial instruments across More ❯

pipelines. Required Skills and Qualifications Core Technical Skills Skill Area Requirements Programming Strong proficiency in Python for data manipulation and scripting. Familiarity with standard Python data libraries (e.g., Pandas, NumPy ). Database Expert-level proficiency in SQL (Structured Query Language). Experience writing complex joins, stored procedures, and performing performance tuning. Big Data Concepts Foundational understanding of Big Data architecture More ❯

pipelines. Required Skills and Qualifications Core Technical Skills Skill Area Requirements Programming Strong proficiency in Python for data manipulation and scripting. Familiarity with standard Python data libraries (e.g., Pandas, NumPy ). Database Expert-level proficiency in SQL (Structured Query Language). Experience writing complex joins, stored procedures, and performing performance tuning. Big Data Concepts Foundational understanding of Big Data architecture More ❯

in recommendation systems and deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work More ❯