76 to 100 of 269 Pandas Jobs

applying machine learning or AI techniques Strong programming skills in Python and TypeScript (this is essential) Experience with common Python data/ML libraries (e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Experience building or contributing to TypeScript codebases (e.g. Node.js backends, React frontends, or internal tools) Hands-on exposure to AWS (e.g. EC2, S3, IAM; bonus points for Lambda, ECS More ❯

applying machine learning or AI techniques Strong programming skills in Python and TypeScript (this is essential) Experience with common Python data/ML libraries (e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Experience building or contributing to TypeScript codebases (e.g. Node.js backends, React frontends, or internal tools) Hands-on exposure to AWS (e.g. EC2, S3, IAM; bonus points for Lambda, ECS 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 ❯

Stack Data Scientist, with a track record of taking ML projects from conception to deployment in a cloud environment (AWS preferred). Strong proficiency in Python for data science (Pandas, NumPy, Scikit-learn) and SQL (PostgreSQL is a plus). Hands-on experience with the full data lifecycle: data ingestion (e.g., web-scraping with BeautifulSoup, Scrapy, or Selenium), data wrangling More ❯

quantification, model evaluation, and statistical inference is highly valued. Python expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data More ❯

quantification, model evaluation, and statistical inference is highly valued. Python expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data More ❯

as a software engineer or a data engineer and a strong passion to learn. BS/MS in Computer Science or equivalent experience in related fields. Experience in Python, Pandas, PySpark, and Notebooks. SQL knowledge and experience working with relational databases including schema design, access patterns, query performance optimization, etc. Experience with data pipeline technologies like AWS Glue, Airflow, Kafka More ❯

data cycle. - Proven Experience working with AWS data technologies (S3, Redshift, Glue, Lambda, Lake formation, Cloud Formation), GitHub, CI/CD - Coding experience in Apache Spark, Iceberg or Python (Pandas) - Experience in change and release management. - Experience in Database Warehouse design and data modelling - Experience managing Data Migration projects. - Cloud data platform development and deployment. - Experience of performance tuning in 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 ❯

Requirements 2-4 years' experience in applied machine learning and generative AI, including work with large language models. Strong Python programming skills with experience in core ML libraries (numpy, pandas, scikit-learn, boosting methods). Proven ability to design, test, and deploy production-ready machine learning solutions. Hands-on experience in data processing and feature engineering for complex datasets. A More ❯

strategies Provide data-driven recommendations to improve engagement metrics Requirements Experience in Customer Marketing Data Science, including applied statistics and machine learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku, Databricks Experience with ML Ops, including deployment and monitoring Ability to work cross-functionally More ❯

strategies Provide data-driven recommendations to improve engagement metrics Requirements Experience in Customer Marketing Data Science, including applied statistics and machine learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku, Databricks Experience with ML Ops, including deployment and monitoring Ability to work cross-functionally More ❯

Analytical mindset with interest in systematic trading and quantitative research Nice to Have Exposure to trading systems, market data, or algo/bot development Familiarity with numerical libraries (NumPy, Pandas, Numba, etc.) Understanding of version control, CI/CD, or distributed computing More ❯

Analytical mindset with interest in systematic trading and quantitative research Nice to Have Exposure to trading systems, market data, or algo/bot development Familiarity with numerical libraries (NumPy, Pandas, Numba, etc.) Understanding of version control, CI/CD, or distributed computing 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 ❯

deployable AI solutions Key Skills & Experience Required Senior-level experience in data science or a quantitative field Proficient programming skills (Python preferred); familiarity with core data science libraries (NumPy, Pandas, Scikit-Learn) Experience with deep learning tools (e.g. PyTorch, TensorFlow, or similar) Strong mathematical/statistical background Familiarity with a wide range of data science methods (e.g. ML, NLP, Bayesian More ❯

engineering experience, ideally in Treasury or from a hedge fund/buy-side firm Expertise in Python for designing and optimising complex applications Strong proficiency in data tools like Pandas, NumPy, SQL, and real-time data processing Familiarity with DevOps practices, including Linux, cloud platforms, and CI/CD (Docker/Kubernetes is desirable but not essential). Strong communication More ❯

engineering experience, ideally in Treasury or from a hedge fund/buy-side firm Expertise in Python for designing and optimising complex applications Strong proficiency in data tools like Pandas, NumPy, SQL, and real-time data processing Familiarity with DevOps practices, including Linux, cloud platforms, and CI/CD (Docker/Kubernetes is desirable but not essential). Strong communication More ❯

Looking For: Essential: Experience with LLMs (GPT, BERT) and NLP tasks. Fine-tuning pre-trained models for domain-specific applications. Strong Python skills (OOP, PyTorch, Hugging Face, scikit-learn, Pandas, NumPy). Deploying models with Docker, Kubernetes, or serverless platforms. Familiarity with CI/CD, MLOps, and cloud platforms (AWS preferred). Desirable: Named entity recognition, recommendation systems, or image More ❯

Looking For: Essential: Experience with LLMs (GPT, BERT) and NLP tasks. Fine-tuning pre-trained models for domain-specific applications. Strong Python skills (OOP, PyTorch, Hugging Face, scikit-learn, Pandas, NumPy). Deploying models with Docker, Kubernetes, or serverless platforms. Familiarity with CI/CD, MLOps, and cloud platforms (AWS preferred). Desirable: Named entity recognition, recommendation systems, or image More ❯

modern data platforms and cloud technologies. Key Responsibilities: Develop, test, and deploy scalable data engineering solutions using Python . Build and maintain data pipelines leveraging libraries such as NumPy, pandas, BeautifulSoup, Selenium, pdfplumber, and Requests . Write and optimize complex SQL queries and manage databases including PostgreSQL . Integrate and automate workflows using DevOps tools (e.g., CI/CD, Jenkins More ❯

modern data platforms and cloud technologies. Key Responsibilities: Develop, test, and deploy scalable data engineering solutions using Python . Build and maintain data pipelines leveraging libraries such as NumPy, pandas, BeautifulSoup, Selenium, pdfplumber, and Requests . Write and optimize complex SQL queries and manage databases including PostgreSQL . Integrate and automate workflows using DevOps tools (e.g., CI/CD, Jenkins More ❯