76 to 100 of 249 Permanent Pandas Jobs

life cycle processes and tools: CI/CD, version control (git), testing frameworks, MLOps. Experience with data science Python libraries such as Scikit-learn, Pandas, NumPy, Pytorch etc. Comfortable working with Docker and containerised applications Experience using AWS or similar cloud computing platform Experience delivering machine learning solutions within regulated ...

products. (We typically use Heroku, but experience with AWS, GCP or others is very welcome.) Experience using Python libraries for data analysis, such as pandas, scikit, numpy, nltk, matplotlib, jupyter. Ability to communicate effectively on both technical and non-technical levels, and to cultivate a supportive environment for more junior ...

professional experience building quantitative trading tools, models, or analytics in a financial or commodities setting Strong Python skills in a scientific computing environment (numpy, pandas, scipy, statsmodels or similar) Solid grounding in statistics, time series analysis, and data modeling; machine learning experience is a plus Experience building data pipelines ...

Strong understanding of OOP concepts, design patterns, SOLID principles, algorithms, data structures, and ability to write clean, maintainable code Backend Development – Proficiency in Python (Pandas, FastAPI, Flask) for building enterprise applications, data processing and API development Database Technologies – Hands‐on experience with RDBMS databases (e.g., Oracle, PostgreSQL) and NoSQL databases ...

Extensive experience with traditional ML algorithms, Deep Learning frameworks, and complex time-series forecasting. Experience developing production-grade Python code: High proficiency in Python, Pandas, and modern software practices (testing, clean code, code review, CI/CD). Data & Pipeline Expertise: Hands-on experience optimizing data models and access patterns ...

commercial setting. Skilledat working with non‐technical stakeholders to scope, design and delivereffective ML solutions. Insert skills.Proficientwith the Python data science stack (pandas, scikit‐learn, Jupyter, etc.)and version control tools like Git. Exposureto LLMOps, model monitoring, CI/CD and related technologies (Docker, MLflow,Kubernetes, FastAPI, etc.). Knowledgeand ...

Strong understanding of OOP concepts, design patterns, SOLID principles, algorithms, data structures, and ability to write clean, maintainable code • Backend Development – Proficiency in Python (Pandas, FastAPI, Flask) for building enterprise applications, data processing, and API development • Frontend Development – Experience with ReactJS , Node.js, TypeScript/JavaScript, and modern web development practices ...

sound validation, and an instinct for leakage, bias and overfitting in messy real-world data. Strong ML fundamentals across classical ML and deep learning (pandas, scikit-learn, XGBoost or LightGBM, PyTorch), and the judgement to tell when a language model is the wrong tool. A deep understanding of how modern ...

Skilled at working with non‐technical stakeholders to scope, design and deliver effective ML solutions. Insert skills. Proficient with the Python data science stack (pandas, scikit‐learn, Jupyter, etc.) and version control tools like Git. Exposure to LLMOps, model monitoring, CI/CD and related technologies (Docker, MLflow, Kubernetes, FastAPI ...

Strong knowledge of modern data science and machine learning techniques, including supervised and unsupervised learning. Proficiency in the Python data science ecosystem (e.g. NumPy, Pandas, scikit-learn, PyTorch, TensorFlow). Experience with big data and distributed analytics platforms (e.g. Spark, Hadoop). Hands-on experience with Natural Language Processing, graph ...

applying causal inference techniques (e.g., diff‐in‐diff, IV, matching) where randomization is impractical Fluency in Python and core data/ML libraries (pandas, NumPy, scikit‐learn, PyTorch or TensorFlow), combined with solid software engineering practices (clean code, testing, version control, code review) Proficient with large‐scale data: strong ...

platform: Data pipelines, Power BI semantic models, OneLake governance ERP: Dynamics 365 F&O (required), Microsoft Great Plains (preferred) Programming: Python (data manipulation, automation, pandas/numpy), SQL (T-SQL, complex queries, stored procedures) Reporting & BI: Power BI (DAX, Power Query, dataset management), SSRS, Excel (advanced, including Power Query ...

relevant years of experience: 8+ Detailed Job Description: JD: • 8+ years of experience in python scripting. • Exposed to python packages such as NumPy, pandas, polars beautiful soup, Selenium, Requests etc. • Experience in building native python ETL pipelines for scraping/processing. • Good to have experience in processing big data. • Proficient ...

batch and online) Solid understanding of statistical methods, including GLMs and modern ML techniques Proven ability to build and deploy production-quality Python applications (pandas, scikit-learn) Experience with DevOps and ML tooling, including Kubernetes, Docker, CI/CD, and git-based workflows Familiarity with cloud platforms (AWS) and cloud ...

Python skills by building a car price prediction app using Python and Streamlit. Step 6 Python for Data Learn essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualisation. Step 7 AI Sentiment Analysis Project Work with Hugging Face to build a sentiment analysis classifier using real ...

Python skills by building a car price prediction app using Python and Streamlit. Step 6 Python for Data Learn essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualisation. Step 7 AI Sentiment Analysis Project Work with Hugging Face to build a sentiment analysis classifier using real ...

Python skills by building a car price prediction app using Python and Streamlit. Step 6 – Python for Data Learn essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualisation. Step 7 – AI Sentiment Analysis Project Work with Hugging Face to build a sentiment analysis classifier using real ...

Python and SQL Strong foundation in mathematics and statistics Proficiency in Python, including relevant libraries for data analysis and machine learning (e.g. sklearn, Pandas, NumPy) Knowledge of data analysis techniques, such as statistical modelling and visualisation, to draw insight from data Familiarity with Git as part of the development process ...

Experience in managing data and implementing data science processes. Experience in translating customer requirements intotechnical requirements and development objectives. Pythonexpertisecovering standard data sciencelibraries (NumPy, pandas, matplotlib, Scikit-learn,PyTorch, etc),web frameworks (Flask or FastAPI) and visualisation(Dash,Plotly,&Streamlit). Proficient inSQL. Working with unstructured data Building customer ...

memory management, and interoperability protocols such as MCP Experience with machine learning frameworks and data science packages such as PyTorch, TensorFlow, scikit‐learn, NumPy, pandas, SciPy, and statsmodels Experience deploying to AWS, including services such as SageMaker and Bedrock, and operating production large language model and machine learning workloads with ...

frameworks, and deployment pipelines Solid foundations and understanding of production‐grade, cloud‐native platform and infrastructure requirements, design, and implementation. Strong data manipulation skills (pandas, SQL) and understanding of evaluation strategies for LLM‐based systems Ability to work with ambiguity and optimise non‐deterministic systems through a process of experimentation ...

exceptions. Technical/Analytical Skills Proficiency in SQL (advanced querying, reconciliation logic, data validation). Strong proficiency in Python for data analysis and automation (pandas, validation frameworks, scripting). Experience supporting or validating ETL/ELT pipelines and data quality frameworks (rules, thresholds, exception handling). Exposure to lineage ...

financial services, lending, payments, or another decision‐intensive domain. Proficiency in SQL, Python and core data science and machine learning libraries, such as NumPy, Pandas and Scikit‐Learn. Strong practical experience with machine learning model development, deployment, monitoring, and iteration in production environments, including cloud‐based model training, archiving, serving ...

5+ years of experience in Data Engineering, Analytics Engineering, Data Science, or Data Quality Engineering. Expert-level SQL skills. Strong Python experience, including pandas or similar data processing libraries. Experience building or supporting ETL pipelines and data ingestion workflows. Strong understanding of relational databases, data modeling, and schema design. Ability ...

5+ years of experience in Data Engineering, Analytics Engineering, Data Science, or Data Quality Engineering. Expert-level SQL skills. Strong Python experience, including pandas or similar data processing libraries. Experience building or supporting ETL pipelines and data ingestion workflows. Strong understanding of relational databases, data modeling, and schema design. Ability ...