201 to 225 of 235 NumPy Jobs

learning models to production Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, or Engineering - or equivalent Proficiency in Python and relevant data science libraries (NumPy, pandas, scikit-learn etc.) Experience with SQL, Power BI, Git & GitHub Strong knowledge of Machine Learning Algorithms and respective theory Ability to work within a team, collaborating effectively with colleagues More ❯

demonstrable proficiency across: - CORE EXPERIENCE : Strong command of Generative AI, LLMs, and RAG principles. Proven experience managing the end-to-end ML lifecycle. - TECH STACK: Proficiency in Python (Pandas, NumPy, LangChain). Deep familiarity with the [Specific Vendor]’s [Cloud Environment] AI and Data Stack (Azure ML, Azure OpenAI, Databricks). - SPECIALIZATION: Expertise in Vector databases (FAISS, Pinecone, Azure AI More ❯

ECCV) Hands-on experience training, evaluating, and deploying state-of-the-art computer vision models (e.g., YOLO, Vision Transformers, Eva) Strong Python skills and familiarity with libraries such as NumPy, pandas, scikit-learn, TensorFlow, and PyTorch Experience with ML workflows, MLOps, and best practices in model validation and evaluation Solid understanding of evaluation metrics such as Accuracy, Recall, F1, IoU More ❯

ECCV) Hands-on experience training, evaluating, and deploying state-of-the-art computer vision models (e.g., YOLO, Vision Transformers, Eva) Strong Python skills and familiarity with libraries such as NumPy, pandas, scikit-learn, TensorFlow, and PyTorch Experience with ML workflows, MLOps, and best practices in model validation and evaluation Solid understanding of evaluation metrics such as Accuracy, Recall, F1, IoU More ❯

They’re Looking For You’ve led the delivery of applied machine learning projects, ideally across commercial or regulated sectors Strong Python skills and comfort using core libraries (e.g. NumPy, Pandas), plus familiarity with deep learning tooling like PyTorch Expertise in a wide range of ML methods, including supervised and unsupervised learning, time series, or NLP and LLM/GenAI More ❯

They’re Looking For You’ve led the delivery of applied machine learning projects, ideally across commercial or regulated sectors Strong Python skills and comfort using core libraries (e.g. NumPy, Pandas), plus familiarity with deep learning tooling like PyTorch Expertise in a wide range of ML methods, including supervised and unsupervised learning, time series, or NLP and LLM/GenAI More ❯

They’re Looking For You’ve led the delivery of applied machine learning projects, ideally across commercial or regulated sectors Strong Python skills and comfort using core libraries (e.g. NumPy, Pandas), plus familiarity with deep learning tooling like PyTorch Expertise in a wide range of ML methods, including supervised and unsupervised learning, time series, or NLP and LLM/GenAI More ❯

data science methodologies Your Background 3+ years of industry experience as a Data Scientist , plus a strong academic foundation Python Data Science Stack : Advanced proficiency in Python , including Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch More ❯

data science methodologies Your Background 3+ years of industry experience as a Data Scientist , plus a strong academic foundation Python Data Science Stack : Advanced proficiency in Python , including Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch More ❯

Scientist, your background will include: 3+ years industry experience in a Data Science role and a strong academic background Python Data Science Stack: Advanced proficiency in Python , including pandas , NumPy , scikit-learn , and Jupyter Notebooks . Statistical & ML Modelling: Strong foundation in statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression More ❯

ideas with the wider team, learn from and contribute to the body of knowledge. Key Skills Experience using Python and SQL. Strong proficiency with data manipulation including packages like NumPy, Pandas. Knowledge of machine learning techniques and their respective pros and cons. Confident communicator and contributes effectively within a team environment. Self driven and willing to lead on projects/ More ❯

and graph analytics and hands on experience and solid understanding of machine learning and deep learning methods. Extensive experience with machine learning and deep learning toolkits (e.g., TensorFlow, PyTorch, NumPy, Scikit Learn, Pandas). Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals. Experience with big More ❯

environment where research rapidly turns into real-world applications No domain expertise required — curiosity and adaptability are key Technical skillset: Must-have: Python, data science/ML libraries (pandas, NumPy, scikit-learn, XGBoost, PyTorch, Hugging Face), experiment design, evaluation metrics Nice-to-have: LLM fine-tuning, prompt engineering, SQL/PostgreSQL How We Work We live for our customers — aiming More ❯

environment where research rapidly turns into real-world applications No domain expertise required — curiosity and adaptability are key Technical skillset: Must-have: Python, data science/ML libraries (pandas, NumPy, scikit-learn, XGBoost, PyTorch, Hugging Face), experiment design, evaluation metrics Nice-to-have: LLM fine-tuning, prompt engineering, SQL/PostgreSQL How We Work We live for our customers — aiming More ❯

language assessments and predictive models. Optimize models for performance, scalability, and accuracy. Qualifications: Deep knowledge of neural networks (CNNs, RNNs, LSTMs, Transformers). Strong experience with data tools (Pandas, NumPy, Apache Spark). Solid understanding of NLP algorithms. Experience integrating ML models via RESTful APIs. Familiarity with CI/CD pipelines and deployment automation. Strategic thinking around architecture and trade More ❯

language assessments and predictive models. Optimize models for performance, scalability, and accuracy. Qualifications: Deep knowledge of neural networks (CNNs, RNNs, LSTMs, Transformers). Strong experience with data tools (Pandas, NumPy, Apache Spark). Solid understanding of NLP algorithms. Experience integrating ML models via RESTful APIs. Familiarity with CI/CD pipelines and deployment automation. Strategic thinking around architecture and trade More ❯

language assessments and predictive models. Optimize models for performance, scalability, and accuracy. Qualifications: Deep knowledge of neural networks (CNNs, RNNs, LSTMs, Transformers). Strong experience with data tools (Pandas, NumPy, Apache Spark). Solid understanding of NLP algorithms. Experience integrating ML models via RESTful APIs. Familiarity with CI/CD pipelines and deployment automation. Strategic thinking around architecture and trade More ❯

or C# , ensuring scalability and performance. Implement and manage ETL (Extract, Transform, Load) processes to support data integration and transformation workflows. Utilize Python data processing libraries such as pandas , NumPy , or PySpark to handle large datasets efficiently. Lead technical design discussions and mentor junior developers. DevOps & Infrastructure Design and implement CI/CD pipelines using GitHub Actions, Jenkins, or similar … ML integration and data-driven orchestration frameworks. Experience with ETL processes and SQL Server Integration Services (SSIS) is highly beneficial. Proficiency with Python data processing libraries such as pandas, NumPy, or PySpark is considered a strong asset. Nice to have: experience building and deploying UI frameworks like React or Angular. The Person: Strong interpersonal skills to work effectively with PMO More ❯

Machine Learning Engineer Machine Learning Engineer – AI for Advanced Materials – Oxford (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We’re looking for a Machine Learning Engineer to join a rapidly scaling deep-tech company that’s reinventing how the … Machine Learning Engineers with experience in some or all of the following (full training provided to fill any gaps): Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, Pandas, NumPy, SciPy, CI/CD, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform, Azure, AWS, GCP, Git, and Agile methodologies. Join a team that’s fusing AI, science, and engineering to push More ❯

Machine Learning Engineer Machine Learning Engineer - AI for Advanced Materials - Oxford/Remote (UK) (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We're looking for a Machine Learning Engineer to join a rapidly scaling deep-tech company that's … Machine Learning Engineers with experience in some or all of the following (full training provided to fill any gaps): Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, Pandas, NumPy, SciPy, CI/CD, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform, Azure, AWS, GCP, Git, and Agile methodologies. Join a team that's fusing AI, science, and engineering to push More ❯

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

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

funding late this year. They code in Python, and React on the Frontend. Tech & Data Science stack: Kubernetes & Docker on Google Cloud Python 3: Pandas, RabbitMQ, Celery, Flask, SciPy, NumPy, Dash, Plotly, Matplotlib Javascript, React, Redux PostgreSQL, Redis Prometheus, Alert Manager, DataDog If you joined the company in a Data Science role you would be working on sophisticated pricing algorithms … know when to be pragmatic to ensure the best business outcomes You'll be a coder in Python, C++ or Java Experience of productionizing analytics code pandas, scipy and numpy If your a Data Scientist looking to go on an exciting new journey with an early stage startup, and the opportunity to work on advanced pricing algorithms is something that More ❯

hear from you if you Have an advanced degree in Computer Science, Mathematics or a similar quantitative discipline Are proficient with Python, including open-source data libraries (e.g Pandas, Numpy, Scikit learn etc.) Have experience productionising machine learning models Are an expert in one of predictive modeling, classification, regression, optimisation or recommendation systems Have experience with Spark Have knowledge of … Models (fine tuning, RAG, agents) Experience with graph technology and/or algorithms, understanding of NLP algorithms Our technology stack Python and associated ML/DS libraries (scikit-learn, NumPy, LightGBM, Pandas, TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc. MLOp/DevOps: Terraform, Docker, Airflow, MLFlow, NewRelic The interview process Recruiter Call (30 minutes) Meeting a Machine More ❯

unit and integration testing, promoting long-term stability and maintainability. Apply SOLID principles and appropriate design patterns to ensure scalable and maintainable architecture. Work with modern technologies including SciPy, NumPy, Pandas, SQL, cloud platforms, Docker, Kubernetes, and CI/CD tools. Collaborate with cross-functional teams (data science, consultants, development) across the Netherlands, UK, and US, with a focus on … concepts and models. Experience in developing analytic and optimization solutions in a product environment. Valuable: Experience with marketing mix modeling, forecasting, or econometric/statistical modeling. Valuable: Familiarity with NumPy, Pandas, SQL, cloud platforms, Docker, Kubernetes, CI/CD tools. Excellent communication, teamwork, and problem-solving skills. What we offer The opportunity to shape the analytics engine that drives marketing More ❯