1 to 25 of 95 Remote NumPy Jobs

Science, Engineering, AI, or related field. 7+ years of professional software development experience, with at least 3 years in AI/ML. Strong proficiency in Python , including libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch . Solid understanding of ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP More ❯

build tools Strong Linux and Git skills Desirable skills: AWS or cloud platform experience WebSocket and real-time data handling DevOps tooling (Docker, Kubernetes, Ansible) Data science packages (pandas, numpy, matplotlib) Modern C++ knowledge (C++17 and later) Interest in sports betting, financial services or trading platforms Benefits: Working alongside other extremely talented and driven engineers Extremely lucrative salary, bonus and More ❯

of data processing and ML jobs, including the use of IAC to build data pipelines Expert knowledge of Python. An excellent knowledge of basic machine learning libraries, such as NumPy, SciPy, Pandas, Dask, PyTorch, Tensorflow, etc. A proven track record of linking data from multiple systems for scalable productionised solutions with security and monitoring best practices. Experienced with Cloud Security More ❯

reliability and innate curiosity to learn new things Preferred qualifications Background in traditional finance or digital assets, ideally in trading domain Hands-on experience with Python libraries and frameworks (NumPy, Pandas, Airflow, FastAPI, Flask, SQLAlchemy) Highly proficient in asynchronous, event driven distributed systems Working knowledge of cloud-native architectures, GCP preferred Experience in Go and working with real-time data More ❯

reliability and innate curiosity to learn new things Preferred qualifications Background in traditional finance or digital assets, ideally in trading domain Hands-on experience with Python libraries and frameworks (NumPy, Pandas, Airflow, FastAPI, Flask, SQLAlchemy) Highly proficient in asynchronous, event driven distributed systems Working knowledge of cloud-native architectures, GCP preferred Experience in Go and working with real-time data More ❯

AI/Data Developer for an urgent contract assignment. Key Requirements: Proven background in AI and data development Strong proficiency in Python , including data-focused libraries such as Pandas, NumPy, and PySpark Hands-on experience with Apache Spark (PySpark preferred) Solid understanding of data management and processing pipelines Experience in algorithm development and graph data structures is advantageous Active SC More ❯

of data structures, algorithms, and software engineering best practices Track record of designing scalable, production-grade systems Excellent problem-solving, collaboration, and communication skills Nice to Have Experience with NumPy, Pandas, Cython, or Numba Exposure to market microstructure, risk modelling, or quantitative research Experience developing and maintaining live trading bots or algo execution systems Background in mentoring or technical leadership More ❯

in computer science, or a proven record of delivering quality software Curious, proactive, and motivated to write great code and improve as a developer Bonus Points For Experience With NumPy, Pandas HTML, CSS, JavaScript Git LaTeX Cybersecurity best practices Financial, legal, or compliance-related tech Location & Setup The engineering team is fully remote, with the existing developers based in South More ❯

Job Description:- Data Engineer (Python enterprise developer): 6+ years of experience in python scripting. Proficient in developing applications in Python language. Exposed to python-oriented Algorithms libraries such as NumPy, pandas, beautiful soup, Selenium, pdfplumber, Requests etc. Proficient in SQL programming, Postgres SQL. Knowledge on DevOps like CI/CD, Jenkins, Git. Experience working with AWS(S3) and Azure Databricks. 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

learning, neural networks, NLP, etc.). Hands-on experience with frameworks such as TensorFlow , PyTorch , or scikit-learn . Proficiency in Python and familiarity with common data science libraries (NumPy, pandas, etc.). Solid grasp of statistics, linear algebra, and probability. Excellent problem-solving skills and ability to communicate complex ideas clearly. Desirable Skills Experience with deep learning architectures (CNNs More ❯

experience (3–5+ years) as a Machine Learning Engineer , Data Scientist , or similar role. Strong programming skills in Python (experience with libraries such as TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience More ❯