26 to 50 of 193 NumPy Jobs

quantitative emphasis in mathematics, engineering, or computer science. Minimum of 5 years of experience in Python development, with a strong understanding of object-oriented programming principles. Experience with pandas, numpy, matplotlib, scipy, and web frameworks like Flask or FastAPI is a plus. Good understanding of relational databases such as Oracle and MS SQL Server. Previous experience in front-office roles More ❯

and apply them to real-world use cases Contribute to code reviews, testing, and documentation Required Skills & Experience Strong proficiency in Python and its AI/ML ecosystem (e.g., NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow) Experience with machine learning model development , training, and deployment Familiarity with LLMs , NLP , or computer vision techniques Solid understanding of software engineering principles and version More ❯

ideally in front office finance , fintech , high-frequency trading , or technology-driven startups Expert-level Python skills - designing, debugging, and optimizing complex applications Very strong data capabilities with Pandas , NumPy , SQL , real-time data processing, and ETL Familiarity with DevOps practices including Linux , cloud platforms , CI/CD , and containerization (Docker/Kubernetes desirable) Proven ability as a live problem More ❯

risk analysis projects. Drive innovation and resource efficiency, contributing to the continuous development of their consulting offering and analytical capabilities as the company grows. Skills & Experience Python (incl. pandas, numpy, fastapi, dash/plotly) Database development: e.g. SQL, PostgreSQL, SQLAlchemy, data warehousing, ETL pipelines Cloud computing & DevOps: e.g. AWS (EC2, Lambda, S3), Docker, CI/CD, serverless architecture Frontend development More ❯

testable, modular code. Strong understanding of data structures, data modeling, and software architecture Data Science Library Knowledge: Deep understanding of key Data Science and Machine Learning libraries (e.g., pandas, NumPy, scikit-learn, TensorFlow), with a preference for PySpark experience Model Productionisation: Experience in taking Machine Learning models from development to production CI/CD and MLOps Experience : Familiarity with Continuous More ❯

Experience with LLM application design and deployment. Strong software engineering skills, including version control (Git), code reviews, and unit testing. Familiarity with common data science libraries and tools (e.g., NumPy, Pandas, Scikit-learn, Jupyter). Experience in setting up and managing continuous integration and continuous deployment pipelines. Proficiency with containerization technologies (e.g., Docker, Kubernetes). Experience with cloud services (e.g. More ❯

client stakeholders Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks (Desirable): Azure ML, AWS Sagemaker, HuggingFace, OpenAI APIs Version Control More ❯

client stakeholders Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks (Desirable): Azure ML, AWS Sagemaker, HuggingFace, OpenAI APIs Version Control More ❯

client stakeholders Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks (Desirable): Azure ML, AWS Sagemaker, HuggingFace, OpenAI APIs Version Control More ❯

client stakeholders Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks (Desirable): Azure ML, AWS Sagemaker, HuggingFace, OpenAI APIs Version Control More ❯

Mandatory required high proficiency in ETL, SQL and database management Experience with AWS services like Glue, Athena, Redshift, Lambda, S3 Python programming experience using data libraries like pandas and numpy etc Interest in machine learning, logistic regression and emerging solutions for data analytics You are comfortable working without direct supervision on outcomes that have a direct impact on the business More ❯

management or mentorship. Have good communication skills. Nice to have Experience deploying LLMs and agent-based systems Our technology stack Python and associated ML/DS libraries (scikit-learn, numpy, pandas, LightGBM, LangChain/LangGraph, TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, ECR, Athena, etc. MLOps: Terraform, Docker, Spacelift, Airflow, MLFlow Monitoring: New Relic CI/CD: Jenkins, Github More ❯

management or mentorship. Have good communication skills. Nice to have Experience deploying LLMs and agent-based systems Our technology stack Python and associated ML/DS libraries (scikit-learn, numpy, pandas, LightGBM, LangChain/LangGraph, TensorFlow, etc...) PySpark AWS cloud infrastructure: EMR, ECS, ECR, Athena, etc. MLOps: Terraform, Docker, Spacelift, Airflow, MLFlow Monitoring: New Relic CI/CD: Jenkins, Github More ❯

approach to working in cross-functional Agile squads. Your experience: Expert-level fluency in Python (required) with deep experience in ML, OR, and DS libraries (e.g. scikit-learn, pandas, numpy, Gurobi). Strong knowledge of machine learning and optimisation techniques-including supervised, unsupervised learning, and operations research methods. Solid background in software engineering for data science products: version control (Git More ❯

approach to working in cross-functional Agile squads. Your experience: Expert-level fluency in Python (required) with deep experience in ML, OR, and DS libraries (e.g. scikit-learn, pandas, numpy, Gurobi). Strong knowledge of machine learning and optimisation techniques-including supervised, unsupervised learning, and operations research methods. Solid background in software engineering for data science products: version control (Git 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 ❯

industry settings, such as financial forecasting, operations optimisation, or customer segmentation. Comfortable with key ML frameworks (such as Scikit-learn, TensorFlow, PyTorch, Hugging Face) and data manipulation tools (Pandas, NumPy), as well as version control, containerisation, and ML deployment pipelines. Understands how to apply MLOps principles in production environments To be successful in this role you must have hands-on More ❯

with the ability to write ad-hoc and complex queries to perform data analysis. Experience developing data pipelines and data warehousing solutions using Python and libraries such as Pandas, NumPy, PySpark, etc. You will be able to develop solutions in a hybrid data environment (on-Prem and Cloud). Hands on experience with developing data pipelines for structured, semi-structured More ❯

your technical capabilities in a collaborative environment, this is an excellent opportunity to take the next step in your career. Essential Solid programming knowledge with Python Experience using Pandas, NumPy, Matplotlib, and PyTorch Strong understanding of SQL for querying and data manipulation Familiarity with CI/CD workflows and Git version control Detail-oriented and proactive problem solver Enthusiastic about More ❯

6+ years of hands-on experience in developing and deploying machine learning or AI systems in production environments in financial services. Proficiency in Python and essential libraries (e.g., Pandas, NumPy, TensorFlow, PyTorch, PySpark). Solid foundation in software engineering best practices, including modular code design, version control, and documentation. Excellent communication and collaboration skills, with the ability to work across More ❯

with customer stakeholders to align on requirements and technical implementations Required Qualifications: Active Poly Minimum 3-5 years' experience with: Data Processing Python Libraries such as PySpark, Pandas and Numpy Experience with API development in Python using Python libraries such as FastAPI Experience with Unit Testing Frameworks in PyTest and Mocking Desired Qualifications: Experience with Python ORM tools for database More ❯

/experience analyzing US Treasury and/or European government bond markets preferred Strong proficiency in Python programming and data manipulation libraries and experience dealing with datasets (e.g., Pandas, NumPy, SciPy) Experience with Dash/Plotly or other visualization software highly desirable Experience with database programming languages (e.g. kdb, SQL) Strong knowledge of MS Excel (especially using real-time data More ❯

years of work experience in software developement ( Python and C++ )- Academic degree in computer science, mechatronics, electronic engineering, telecommunication engineering or similar qualification, PhD welcome- Experience with Python (numpy/scipy/matplotlib)- Hands-on experience in AWS- Software debugging skills, understanding of performance optimization- Very good understanding of the software development process, preferably in the automotive area- You are More ❯

in a team setting Eagerness to continuously learn and adapt to evolving tools, stacks, and requirements Nice-to-Have: Exposure to data analysis libraries or scientific computing (e.g., pandas, NumPy, matplotlib, Py-Torch, Tensor-flow, SK-Learn) Basic understanding of machine learning principles or experience with small academic projects Familiarity with cloud platforms (AWS/GCP/Azure), APIs, or More ❯

in a team setting Eagerness to continuously learn and adapt to evolving tools, stacks, and requirements Nice-to-Have: Exposure to data analysis libraries or scientific computing (e.g., pandas, NumPy, matplotlib, Py-Torch, Tensor-flow, SK-Learn) Basic understanding of machine learning principles or experience with small academic projects Familiarity with cloud platforms (AWS/GCP/Azure), APIs, or More ❯