20 of 20 Contract NumPy Jobs

feature development and more on reliability and fixes. Key Skills: Investigating and debugging complex data flow and Machine Learning issues within a live, high impact production environment. Extensive Python, NumPy and Pandas is required for this role. You must demonstrate a deep commercial background in the following areas: Extensive Python: Very strong, production-level Python coding and debugging skills. Production 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 ❯

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 ❯

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 ❯

Stay up-to-date with emerging trends and technologies in the field of data science. Requirements Proven experience as a data scientist using Python and a range of libraries (Numpy, Pandas, Scikit-Learn, Matplotlib, Plotly etc.). Strong expertise in statistical modelling, machine learning, and data mining techniques. Data engineering (pipelines, databases, infrastructure), ideally with AWS experience would be an More ❯

to teams adopting AI tooling. What Youll Bring 5+ years in software/ML engineering, ideally with production deployment experience. Strong Python background, comfortable across data frameworks like Pandas, NumPy, SciPy, Dask, Polars, or PySpark. Proven experience setting up ML pipelines, integrating with AWS/Databricks, and applying CI/CD principles. Solid understanding of time-series forecasting and supervised More ❯

complex datasets. Desirable Experience & Skills: Practical knowledge of data engineering practices (designing and building robust data pipelines) Knowledge of Python and core libraries applicable to data science (eg, pandas, numpy, statsmodels) Hybrid - 3 days from the client's office. More ❯

complex datasets. Desirable Experience & Skills Practical knowledge of data engineering practices (designing and building robust data pipelines) Knowledge of python and core libraries applicable to data science (e.g., pandas, numpy, statsmodel More ❯

mentoring, and peer reviews. Continuously evolve the distributed software stack, improving performance and reliability across platforms. What Youll Bring Strong proficiency in Python , including modern data engineering libraries (Pandas, NumPy, Dask, Polars, PySpark). Experience designing or maintaining distributed systems or microservice architectures . Background as a software engineer or data engineer experience in energy markets is a significant advantage More ❯

for AI models. Collaborate with data scientists, software engineers, and subject-matter experts to deliver innovative solutions. Essential Skills & Experience Advanced Python programming skills with hands-on experience using NumPy, Pandas, Scikit-learn, Langchain, LlamaIndex , and Azure AI Foundry . Proven experience working with Azure cloud services and Generative AI R&D. Strong understanding of machine learning, data modelling , and More ❯

outputs. Stay informed on emerging trends, technologies, and best practices in data science. Experience Required: Proven experience as a Data Scientist with proficiency in Python and libraries such as NumPy, Pandas, Scikit-learn, Matplotlib, and Plotly. Strong background in statistical modelling, machine learning, and data mining, with experience working on time-series data. Knowledge of data engineering principles, including pipelines More ❯

engineers and T&L domain experts Required Skills/Experience The ideal candidate will have the following: Be an expert Python programmer proficient in frameworks/libraries such as: Numpy, Pandas, Scikit-Learn, Langchain, Llamaindex, Azure AI Foundry amongst others. Must have Azure and be an expert with R&D on generative AI techniques Practical experience with GenAI techniques such More ❯

umbrella company for the duration of the contract. You must have several years of experience developing data pipelines and data warehousing solutions using Python and libraries such as Pandas, NumPy, PySpark, etc. You will also have a number of years hands-on experience with cloud services, especially Databricks, for building and managing scalable data pipelines. ETL process expertise is essential. More ❯

concepts and assist the business with evaluations to measure success and estimate value proposition. Essential Criteria: * Extensive experience with Python and data science Python packages (e.g. scikit-learn, pandas, numpy, etc) * Understanding of data science concepts, AI/ML models, evaluation approaches, and data science applications to enhance business processes * Proven hands-on experience in Microsoft Azure ML Studio * Experience More ❯

as a Data Scientist , ideally in a government or large-scale enterprise environment. Strong Python programming skills, including experience with data science and AI/ML frameworks (e.g., Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow). Experience developing and fine-tuning LLMs and working with generative AI tools. Hands-on experience with Microsoft Azure (Azure ML, Databricks, or other AI services More ❯

as a Data Scientist , ideally in a government or large-scale enterprise environment. Strong Python programming skills, including experience with data science and AI/ML frameworks (e.g., Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow). Experience developing and fine-tuning LLMs and working with generative AI tools. Hands-on experience with Microsoft Azure (Azure ML, Databricks, or other AI services More ❯

Experience Current enhanced DV clearance (mandatory) Proven experience in a data science or machine learning role Strong programming skills in Python and familiarity with key data libraries (e.g. pandas, NumPy, scikit-learn) Experience with data wrangling , feature engineering, and model optimisation Understanding of data pipelines , APIs, and production deployment workflows Excellent communication skills and a collaborative approach Why Apply? Work More ❯

Proficient Python Programming Key skills: Functions, classes, and object-oriented programming, List comprehensions, generators, Error handling, Working with virtual environments and package management (pip, venv) Data Manipulation & Analysis (Pandas & NumPy) Key libraries: pandas, numpy, (optional: polars) Key skills: Data cleaning and preprocessing, Handling missing values, grouping, merging, pivoting, aggregations, and SQL Software Engineering Best Practices Key practices: Version control with More ❯