276 to 300 of 618 Pandas Jobs

s or PhD degree in a quantitative field. • Proven experience of large-scale data analysis and hypothesis testing. • Strong proficiency in statistical analysis and predictive modeling. • Proficient in Python (pandas, scipy, numpy, scikit-learn) or R (tidyverse/data.table), along with SQL. • Excellent problem-solving skills and attention to detail. • Strong communication skills with the ability to present complex data More ❯

chance to work on our client projects after successful completion of the program, following a successful interview. 🛠 Master What Employers Actually Want: ✔ Python – Essential for data analysis & automation ✔ NumPy & Pandas – Data manipulation & processing ✔ Matplotlib – Data visualization techniques ✔ TensorFlow – Deep learning and AI applications ✔ SciPy & Scikit-learn – Machine learning framework ✔ SQL – Database querying and management 💡 Why This Program? UK Skills Shortage More ❯

NLP or Computer Vision Solid understanding of fundamentals of statistics, optimization and ML theory. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.) Extensive experience with pytorch, numpy, pandas Knowledge of open source datasets and benchmarks in NLP/Computer Vision Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod More ❯

as Detectron, ConvNext, DETR, DINO or similar. Technical knowledge of relevant ML performance metrics and how to apply them to monitor models. Strong knowledge of Python (such as numpy, pandas, matplotlib, streamlit, and opencv). Strong knowledge of modern programming paradigms (OOP, functional programming etc). Ability to write clean, robust, readable, error handling and error tolerant code. Good knowledge More ❯

proficiency in languages such as Python, C# on Linux, Go, or similar. Utilise Advanced Technical Stack: Leverage our technical stack, including Python, SQL, PostgreSQL, BigQuery, and libraries such as Pandas, NumPy, SciKit-Learn, XGBoost, Matplotlib, and Pytorch, to develop robust and scalable AI/ML solutions. Strong foundation in data structures, algorithms, and problem-solving, with a clear focus on More ❯

processes and tools: CI/CD, version control (git), testing frameworks, MLOps Comfortable working with Docker and containerised applications Experience with data science Python libraries such as Scikit-learn, Pandas, NumPy, Pytorch etc. Experience using AWS or similar cloud computing platform Great communicator - convey complex ideas and solutions in clear, precise and accessible ways Team player who cares about accelerating More ❯

to join a data-driven technical risk management team in designing, developing and deploying quantitative risk models to manage our risk. Our tech stack is comprised of Python (including Pandas, NumPy & Matplotlib), SQL, MongoDB, GitLab and increasingly GCP. As a team of Risk Analysts & Risk Managers, we are looking for someone who can own the DevOps side of the model More ❯

one or more of: deep learning, reinforcement learning, non-convex optimisation, approximate inference, NLP, or Bayesian methods Strong programming skills, ideally in Python, with experience using tools like NumPy, Pandas, JAX, PyTorch or TensorFlow A strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR) or competitive performance in ML challenges such as Kaggle or similar platforms The ability More ❯

in a business setting Educated to Bachelor's degree level or equivalent qualification/relevant work experience Excellent programming skills, predominantly across the Python/Anaconda suite (Scikit-learn, Pandas, Numpy) Excellent analytical and data science skills, including ability to independently drive research Excellent communication skills, both written and verbal, with ability to manage multiple stakeholders (beneficial) Understanding financial markets More ❯

a similar role. Strong programming skills and a good understanding of software engineering principles and clean code practices. Expert-level knowledge of Python for machine learning and data manipulation (pandas, NumPy). Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn More ❯

data science roles Creative problem-solving and solution scoping Strong grasp of mathematical, statistical concepts, and machine learning algorithms Proficiency in Python and data science libraries for example NumPy, Pandas, Scikit-learn, Keras SQL proficiency Experience with cloud environments for example Google Cloud Platform Version control management Ability to work efficiently without compromising quality Effective communication and data storytelling skills More ❯

for C-suite stakeholders and decision-makers. Experienced in using BI tools such as Tableau or Microsoft Power BI. Proficient in Python with strong experience in libraries such as pandas, scikit-learn, and xgboost, and writing clean, modular, and reusable code. Experienced in deploying Python scripts and ML workloads. Familiarity with SQL, including writing complex queries with Common Table Expressions More ❯

work with Python and Django on an increasingly more service-oriented architecture. The rest of the tech stack include Django REST Framework, PostgreSQL, AWS, React.js, Kubernetes, Docker, Redis, Celery, Pandas, Numpy, Git, Jenkins. We have a very large but clean code base as we put significant emphasis on design patterns, code readability, automated testing, maintainability and extendability. Responsibilities Work with More ❯

personally conduct data-driven analysis and guide this work through others. Experienced in using Python and SQL to query and analyse large datasets, with expertise in libraries such as Pandas, NumPy, SciPy, Matplotlib, and Seaborn for data manipulation, statistical analysis, and visualisation. Familiarity with Monte Carlo simulations in Python and/or PyMC3 for Bayesian modelling is a plus. Familiarity More ❯

Experience delivering data science or data engineering solutions into production. You're comfortable writing production-grade code, not just notebooks. Strong Python and SQL skills, including the basic libraries (Pandas, Numpy, ScikitLearn). You value writing clean, maintainable, and tested code. Proven ability to design, build, and maintain data pipelines and tools from scratch that are reliable, maintainable, and scalable. More ❯

science in finance, insurance or Ecommerce is an advantage but not required Experience of deployment in a cloud environment Experience with neural networks and Tensor Flow, CatBoost, XGBoost, SKlearn, Pandas Experience with API development SQL experience Software engineering experience DevOps/MLOps experience Good working understanding of CI/CD If you hold the experience and technical skills outlined above More ❯

and technical teams Direct experience working with social and digital platforms such as Meta, Amazon, Google to onboard datasets an advantage Proficiency working with data in SQL and Python (Pandas, NumPy preferred) Knowledge of statistical testing methodologies Experience with BI tools (Tableau, PowerBI preferred) Experience with cloud computing services & solutions (AWS, Azure, GCP, Amazon Marketing Cloud, Snowflake) Experience working with More ❯

solutions are adopted. Skills and experience required: Strong background in data science, computer science, applied mathematics, statistics, or another quantitative field. Experience using foundational Python data science libraries (e.g. pandas, numpy, scikit-learn, ...). Experience with SQL and working with databases like Postgres and Snowflake. Experience of working within cloud environments (Azure and AWS preferred). Comfortable with sourcing More ❯

Science or Forward Deployed roles, with 2+ years direct customer exposure Advanced Python programming skills, including ability to build ML algorithms from scratch using frameworks like numpy, scipy, pytorch, pandas, sklearn Competent SQL skills including CTEs, joins, and ability to learn advanced concepts as needed Experience working in startup or early-stage environments Track record of delivering end-to-end More ❯

record of leading high-performing teams. Proven ability to build predictive models and extract insights from large, complex datasets. Advanced proficiency in Python and SQL, including libraries such as Pandas, NumPy, SciPy, Matplotlib, and Seaborn. Skilled in developing and deploying credit risk models, including scorecard thresholds, limit optimisation, and loss reduction. Experience with Monte Carlo simulations or Bayesian modelling (e.g. More ❯

Science or Forward Deployed roles, with 2+ years direct customer exposure Advanced Python programming skills, including ability to build ML algorithms from scratch using frameworks like numpy, scipy, pytorch, pandas, sklearn Competent SQL skills including CTEs, joins, and ability to learn advanced concepts as needed Experience working in startup or early-stage environments Track record of delivering end-to-end More ❯

for C-suite stakeholders and decision-makers. Experienced in using BI tools such as Tableau or Microsoft Power BI. Proficient in Python with strong experience in libraries such as pandas, scikit-learn, and xgboost, and writing clean, modular, and reusable code. Experienced in deploying Python scripts and ML workloads. Familiarity with SQL, including writing complex queries with Common Table Expressions More ❯

or Master's in Statistics, Mathematics, Computer Science, Engineering, Physics, Economics or a related quantitative field Strong grounding in probability, statistics and machine learning fundamentals Working knowledge of Python (pandas, NumPy, scikit‐learn), and basic SQL Curiosity, problem‐solving mindset and the ability to present complex ideas simply Right to work in the UK Nice to have (but not essential More ❯

bibliometrics, innovation pathways/lifecycles, and learning more about key areas/topics in AI research. Python for Data Analysis: Proficiency in Python and common data analysis libraries (e.g., Pandas, NumPy, SciPy, Scikit-learn, Matplotlib/Seaborn). Data Visualization/Dashboarding: Experience creating effective dashboards and visualizations using tools like Tableau, Looker, Google Data Studio, or similar. Analytics Engineering More ❯

learning models and data science solutions for clients.+ Strong proficiency in programming languages such as Python, R, and SQL.+ In-depth experience with data manipulation and visualization libraries (e.g., Pandas, NumPy, Matplotlib, etc.).+ Solid understanding of big data technologies (e.g., Hadoop, Spark) and cloud platforms (AWS, Azure, Google Cloud).+ Strong expertise in the full data science lifecycle: data More ❯