46 to 70 of 178 Pandas Jobs

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 ❯

computer science, computer engineering, statistics, applied mathematics, or a related field. Experience with AI frameworks (e.g., TensorFlow, PyTorch, scikit-learn, OpenCV). Familiarity with tools such as NumPy, SciPy, Pandas, Matplotlib. Experience deploying AI models in cloud environments (e.g., AWS, Azure). Strong analytical skills and creative problem-solving ability. Understanding of Agile development methodology. Experience with space domain awareness More ❯

SQL, R ML & AI: NLP, EDA, TensorFlow, PyTorch Tools & Libraries: Git, Matplotlib Strong communication and problem-solving abilities Ability to manage complex technical projects PREFERRED QUALIFICATIONS Scikit-learn, NumPy, Pandas Feature engineering experience Spark Seaborn, Tableau Keras Experience designing or supporting data centers or high-availability server infrastructure Experience working with large datasets or distributed systems KEY RESPONSIBILITIES Design, develop More ❯

years of application development experience using Python. Minimum 3 years of experience building, training and deploying machine learning models. Strong ability to use key Python libraries such as Pandas, Numpy and deep learning libraries like PyTorch or Tensorflow. Bachelor's Degree in Computer Science, Engineering, or related field. Familiarity with cloud technologies, such as AWS, Azure, or GCP and how More ❯

years of application development experience using Python. Minimum 3 years of experience building, training and deploying machine learning models. Strong ability to use key Python libraries such as Pandas, Numpy and deep learning libraries like PyTorch or Tensorflow. Bachelor's Degree in Computer Science, Engineering, or related field. Familiarity with cloud technologies, such as AWS, Azure, or GCP and how More ❯

of experience building production-grade data pipelines and ML infrastructure. Proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch). Strong experience with data engineering tools (e.g., Pandas, SQL, Apache Airflow, Spark). Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Experience working with massive amounts of real-world training data. Familiarity with 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 ❯

Docker/Kubernetes), and distributed training (Spark, Ray, Dask, etc.). Experience iterating models in a production environment is a must. Software Engineering Skills Strong proficiency in Python (numpy, pandas, etc.). Experience with scalable data processing (Spark, Ray, BigQuery). Analytical & Statistical Background Comfortable with advanced experimentation techniques. Understanding of performance measurement in real-world deployments. Soft Skills & Culture More ❯

in Computer Science, Electrical Engineer, Physics or related technical discipline. 3+ years of relevant industry experience in data analytics and machine learning Strong expertise in scientific Python (NumPy, SciPy, Pandas) and modern ML frameworks (PyTorch, TensorFlow, JAX, Scikit-Learn, Keras) Proficiency in SQL and experience with relational or time-series databases Proven experience applying statistical modeling, data analysis, and inference More ❯

and contribute from day one. Expertise in causal inference with observational and experimental data. Strong engineering skills with expertise in R or Python and fluency in data manipulation (SQL, Pandas) and machine learning (scikit-learn, XGBoost, Keras/Tensorflow) tools. An ability to identify and prioritize high-impact problems and deliver solutions that provide reasonable trade-offs between urgency and More ❯

end ML pipelines from data ingestion to model serving for real-world applications used by real people. Strong programming and data skills: Proficiency in Python and its ML ecosystem (pandas, scikit-learn, TensorFlow/PyTorch), with clean and efficient coding practices. Comfortable working with large datasets, writing complex SQL queries, and leveraging modern data processing frameworks. Experience with functional programming More ❯

use case Experience in statistical data analysis and machine learning techniques Familiar with in-depth analysis and evaluation of machine learning models Background with analytical tools such as Jupyter, Pandas, NumPy, Matplotlib Strong understanding of the end-to-end machine learning process Ability to thrive in a collaborative environment and communicate clearly and confidently with partner teams PREFERRED QUALIFICATIONS Experience More ❯

Looking For (Minimum Qualifications) 5+ years of experience delivering production-quality software with increasing levels of ownership and technical leadership. Fluent in Python and expertise with libraries like NumPy, Pandas, and scikit-learn Strong background in statistical analysis and computational mathematics with first principles knowledge of machine learning techniques Fundamental understanding of modern AI/ML technologies and their application More ❯

Looking For (Minimum Qualifications) 8+ years of experience delivering production-quality software with increasing levels of ownership and technical leadership. Fluent in Python and expertise with libraries like NumPy, Pandas, and scikit-learn Strong background in statistical analysis and computational mathematics with first principles knowledge of machine learning techniques Fundamental understanding of modern AI/ML technologies and their application More ❯

ML concepts (e.g., classification, regression, clustering, recommendation systems, NLP, time series analysis, experimentation, model evaluation) Strong SQL skills and experience working with large datasets and data processing tools (e.g., Pandas, Spark) Experience with ML deployment patterns and MLOps principles (e.g., model serving, monitoring, CI/CD for ML, feature stores) Familiarity with cloud platforms (AWS, GCP, Azure) is essential Business More ❯

systems (e.g., AWS, GCP, Azure) including and open systems (e.g. Docker and Kubernetes) and their associated ML services. Experience with Data Science tools and processes (e.g. NumPy, scikit-learn, Pandas, PySpark) Familiarity with ML lifecycle tools like MLflow, Weights & Biases Hands-on experience building Generative AI-powered applications, including Large Language Models Strong analytical and problem-solving skills Ability to More ❯

UI, Vite, esbuild, and Playwright Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace , PyTorch, TensorFlow, and Pandas You'll get competitive perks and benefits , from health & wellness to equity, to help you bring your best self to work. For US based applicants: - The US base salary range More ❯

s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Software Engineering, Mathematics, Statistics, or a related field Strong Python skills and comfort with data-science libraries (NumPy, pandas, scikit-learn) Foundational knowledge of AI/ML concepts and frameworks (PyTorch, TensorFlow, or JAX) Exposure to reinforcement learning or similar methods (deep Q-learning, PPO) through coursework, labs, or More ❯

ML concepts (e.g., classification, regression, clustering, recommendation systems, NLP, time series analysis, experimentation, model evaluation) Strong SQL skills and experience working with large datasets and data processing tools (e.g., Pandas, Spark) Experience with ML deployment patterns and MLOps principles (e.g., model serving, monitoring, CI/CD for ML, feature stores) Familiarity with cloud platforms (AWS, GCP, Azure) is essential Business 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 ❯

problems into technical solutions. • Optimize models for scalability, performance, and accuracy. • Mentor junior engineers and review code for quality and best practices. Required Skills & Experience • Strong proficiency in Python (Pandas, NumPy, Scikit-learn, FastAPI/Flask). • Experience with machine learning workflows: data preprocessing, model training, evaluation, and deployment preferably using Microsoft stack - Azure ML, Azure Data Factory, Synapse Analytics More ❯

Stakeholder Communication: Present insights to data labeling experts and technical teams Required Qualifications Statistical Expertise: Strong foundation in statistical analysis, hypothesis testing, and pattern recognition Programming: Proficiency in Python (pandas, scipy, matplotlib/seaborn) or R for data analysis Data Analysis: Experience with exploratory data analysis and creating actionable insights from complex datasets AI/ML Familiarity: Understanding of LLM 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 ❯