16 to 40 of 95 Pandas Jobs

knowledge, experience and capabilities: Computer science fundamentals: a clear understanding of data structures, algorithms, software design, design patterns and core programming concepts. Experience with the core Python data stack (Pandas, NumPy, Scikit-learn, etc) developed in a commercial setting, an appreciation of pipeline orchestration frameworks (e.g., Airflow, Kubeflow Pipelines, etc), applied knowledge of statistical modelling and/or experience in More ❯

is 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 More ❯

machine 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 More ❯

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, MLflow More ❯

Python, R, or other programming languages commonly used in machine learning. Experience with libraries such as TensorFlow, PyTorch, scikit-learn, etc.Tools: Experience with data processing and manipulation tools (SQL, Pandas, Numpy), cloud platforms (AWS, Azure, GCP), and version control systems (GitDesirable Skills: Knowledge of deep learning, NLP, computer vision, or reinforcement learning.Experience in deploying machine learning models in production environments.Familiarity More ❯

flows and ML issues in live production environments. We're looking for individuals with: Experience: Proven background as a Machine Learning Engineer. Technical Skills: Strong in SQL and Python (Pandas, Scikit-learn, Jupyter, Matplotlib). Data transformation & manipulation : experience with Airflow, DBT and Kubeflow Cloud: Experience with GCP and Vertex AI (developing ML services). Expertise: Solid understanding of computer More ❯

particularly 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 More ❯

basic AI algorithms and explore practical applications of AI. Overview of AI and Machine Learning Types of machine learning (supervised, unsupervised, reinforcement) Introduction to Python Libraries for AI - NumPy, Pandas, Matplotlib Scikit-learn for machine learning Building AI Models Data preprocessing Training and evaluating models Advanced Programming for AI Integration This programme aims to equip participants with the knowledge and More ❯

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 asset More ❯

AzureML, Dataiku, Datarobot, H2O, and Jupyter Notebooks. Hands-on scripting experience with SQL and at least one of the following; Python, Java or Scala. Experience with libraries such as Pandas, PyTorch, TensorFlow, SciKit-Learn or similar. BONUS POINTS FOR HAVING : Experience with GenerativeAI, LLMs and Vector Databases. Experience with Databricks/Apache Spark. Experience implementing data pipelines using ETL tools. More ❯

statistics and machine learning techniques (supervised and unsupervised learning, natural language processing, Bayesian statistics, time-series forecasting, collaborative filtering etc) ? 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, Databricks. ? Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. ? Ability More ❯

learning models at scale. Deep familiarity with core ML concepts (classification, time-series, statistical modeling) and their real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from More ❯

learning models at scale. Deep familiarity with core ML concepts (classification, time-series, statistical modeling) and their real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from More ❯

such as Airflow, Prefect, or Temporal, or by building bespoke pipeline systems for multi-step autonomous processes. You bridge science and engineering — comfortable with scientific computing libraries (NumPy, SciPy, pandas) and familiar with scientific databases and literature formats. What Sets You Apart: You have a research background — perhaps as a former academic researcher or research software engineer in ML/ More ❯

technology frameworks with the team Eagerness for working with third-party/open-source technologies Experience with containerization and CI/CD solutions (e.g. Docker, Devise) Experience working with Pandas, Quantitative Analysis Experience working within the Scrum model Enthusiasm to lead, share new ideas, drive processes and technology frameworks with the team Discover what makes Bloomberg unique - watch our podcast More ❯

learning and cutting edge AI & Agents, honed through extensive practical experience across a range of domains Expert-level proficiency in Python and its data science ecosystem (e.g., scikit-learn, pandas), with the ability to select the right tools for complex problems and set technical standards for the team Advanced, hands-on expertise in SQL and big data platformslike Databricks, used More ❯

scale up? Having added nearly 100 people to the business this year, they're looking for Quantitative Developers with proficiency in Python, and experience with the Python data libraries (Pandas, NumPy) (preferably) from the Trading world. They also are on the look out for candidates who: Have deep familiarity with Python data ecosystem Understanding of Jupyter notebooks Exposure to machine More ❯

scale up Having added nearly 100 people to the business this year, they're looking for Quantitative Developers with proficiency in Python, and experience with the Python data libraries (Pandas, NumPy) (preferably) from the Trading world. They also are on the look out for candidates who: Have deep familiarity with Python data ecosystem Understanding of Jupyter notebooks Exposure to machine More ❯

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 advantage. More ❯

must have: 9+ years of hands-on Python experience (FastAPI, Flask, or Django) 5+ years of AWS experience (Lambda, API Gateway, Boto3, CloudFormation) Strong skills in REST APIs, SQL, Pandas, PyTest, and GitLab CI/CD Exposure to data pipelines, ETL, or event-driven systems Python, AWS Mirgation, and data manipulation will be required.Financial domain experience is a strong plus. More ❯

Technical & Analytical Skills Hands-on experience with large data sets and data profiling. Proficiency in data tools: Power BI, Excel, SQL, Python, Databricks. Familiarity with tools like Great Expectations, Pandas, Plotly, or MS Power Automate is a plus. Able to work collaboratively and independently in a fast-paced environment. Ready to Apply? If you're excited by the opportunity to More ❯

Technical & Analytical Skills Hands-on experience with large data sets and data profiling. Proficiency in data tools: Power BI, Excel, SQL, Python, Databricks. Familiarity with tools like Great Expectations, Pandas, Plotly, or MS Power Automate is a plus. Able to work collaboratively and independently in a fast-paced environment. Ready to Apply? If you're excited by the opportunity to More ❯

the latest developments in AI, machine learning, and data science methodologies. Experienced Needed: Masters or PhD in a STEM subject Proficiency in Python, with experience in libraries such as pandas, scikit-learn, TensorFlow, or PyTorch. Solid SQL skills and experience working with relational databases. Exposure to cloud platforms (AWS, GCP, or Azure) would be advantageous. Strong analytical and problem-solving More ❯

For Proven experience delivering data migration or integration projects , ideally with ERP/CRM systems such as Microsoft Dynamics . Strong technical skills in Python , including libraries such as Pandas and NumPy . Hands-on experience with ETL design, data warehousing, and relational databases (SQL). Advanced proficiency in SQL and data modelling techniques. Knowledge of cloud data platforms (Azure More ❯

expertise in MLOps implementation for deploying, monitoring, and managing ML models in production environments. Proficiency in Python and experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy Strong understanding of cloud technologies and AI/ML platforms, particularly AWS SageMaker. Solid grasp of software engineering principles including design patterns, testing, CI/CD, security, and version More ❯