1 to 25 of 98 Pandas Jobs

quickly and iterate based on feedback, with a focus on turning analytical insights into working software. Familiarity with data tools such as Jupyter notebooks, pandas, or similar data analysis libraries. Curiosity about LLM infrastructure, including LangChain/LangGraph, RAG patterns, and vector search—or eagerness to learn about building systems ...

shared team practices Mentor peers and help grow a high-trust, high-impact engineering culture Tech Stack Python 3.x with FastAPI, Pydantic, SQLAlchemy, Pandas, and NumPy Relational databases: MySQL and PostgreSQL GCP experience is a plus, but not required Modern Middleware is a plus – e.g. Pub/Sub, Memcache Docker ...

domain regulations. Required Skills & Qualifications: Proven experience developing with Streamlit in a professional environment. Strong proficiency in Python, including data manipulation and visualization libraries (Pandas, NumPy, Plotly, etc. Experience working with banking or financial datasets. Familiarity with relational and NoSQL databases, APIs, and data integration techniques. Knowledge of banking processes ...

plus. Proficiency in Python and familiarity with ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch). Strong experience with data manipulation and pipelines using Pandas, NumPy, and SQL. Knowledge of containerized deployments (Docker, Kubernetes) and cloud ML services (AWS SageMaker, GCP Vertex AI, or Azure ML) preferred. Excellent problem-solving skills ...

hyper-scaler platforms, e.g., AWS, Azure, GCP); building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications; C/C++ for computer vision, geometry processing, or scientific computing; software engineering concepts and best practices (e.g., versioning ...

pipelines. 2-5 years of relevant industry experience in software engineering, API design (REST APIs) and microservices architecture Proficiency with data analysis tools (e.g., pandas) and as well experienced within a BI Visualization tool like AWS Quick Suite or Tableau Experience building Data Products, Data Modeling and a solid understanding ...

also open to candidates from other asset classes with genuine interest in power & gas. Strong Python skills and experience with the scientific stack (NumPy, pandas, SciPy, scikit-learn or similar). You write clean, production-grade code, not just notebooks. Solid understanding of stochastic processes, Monte Carlo simulation, and time ...

Financial Services domain, specializing in building scalable data models and delivering actionable insights for risk management, payments, and customer analytics. Skilled in Python (pandas, scikit-learn, PySpark), SQL, and data engineering pipelines to enable predictive and prescriptive analytics. Adept at working with large financial datasets, ensuring data quality, and collaborating ...

Python developer, ideally within high-performance, data-heavy environments. Strong grounding in computer science fundamentals — algorithms, data structures, and system design. Experience with NumPy, Pandas, PySpark, or similar data frameworks. Exposure to financial data, trading systems, or quantitative research is highly advantageous (though not required). Passion for clean, performant ...

Word Embeddings Transformers: BERT, ELMo OpenAI Models: GPT-3.5 Turbo, GPT-4o, GPT-3o Reasoning, text-embedding-ada-002 Libraries & Tools numpy, pandas, scipy, scikit-learn, tensorflow, keras, nltk, matplotlib, seaborn, plotly Programming Languages Python, R, C++, C#, Java, Node.js, HTML, SQL Agentic AI OpenAI Agents SDK, Model Context Protocol ...

actionable insights for trading and risk management. Document system architecture, data flows, and technical processes for transparency and reproducibility. Requirements Strong proficiency in Python (pandas, NumPy, PySpark) and ETL development. Hands-on experience with AWS services (S3, Glue, Lambda) and Databricks. Solid understanding of financial market data, particularly time-series. ...

Data Engineer: Extensive hands-on experience with AWS data services Strong programming skills in Python (including libraries such as PySpark or Pandas) Solid understanding of data modelling, warehousing and architecture design within cloud environments Experience building and managing ETL/ELT workflows and data pipelines at scale Proficiency with ...

ability to solve complex, real-world problems through data science and analytics. Experience coaching and reviewing work of junior team members. Strong Python skills (pandas, numpy, scikit-learn) and a solid grounding in probability and statistics. Deep knowledge of machine learning methods and their practical application. Experience managing multiple ...

/ML, data science, or applied statistics Solid programming skills in Python (or similar languages) Familiarity with data science or AI libraries such as Pandas, NumPy, TensorFlow, PyTorch, or Scikit-learn Strong problem-solving ability and analytical thinking A willingness to learn and work collaboratively in a team environment Nice ...

denoising/voice enhancement techniques. Knowledge of speaker diarization, speaker recognition, and multi-language ASR challenges. Experience with data analysis and wrangling (e.g., Pandas, NumPy, Jupyter) to evaluate model performance. Understanding of cloud deployment and DevOps basics (e.g., Docker, Kubernetes, serverless workloads). Comfort working in a fast-paced environment ...

also open to candidates from other asset classes with genuine interest in power & gas. Strong Python skills and experience with the scientific stack (NumPy, pandas, SciPy, scikit-learn or similar). You write clean, production-grade code, not just notebooks. Solid understanding of stochastic processes, Monte Carlo simulation, and time ...

also open to candidates from other asset classes with genuine interest in power & gas. Strong Python skills and experience with the scientific stack (NumPy, pandas, SciPy, scikit-learn or similar). You write clean, production-grade code, not just notebooks. Solid understanding of stochastic processes, Monte Carlo simulation, and time ...

also open to candidates from other asset classes with genuine interest in power & gas. Strong Python skills and experience with the scientific stack (NumPy, pandas, SciPy, scikit-learn or similar). You write clean, production-grade code, not just notebooks. Solid understanding of stochastic processes, Monte Carlo simulation, and time ...

also open to candidates from other asset classes with genuine interest in power & gas. Strong Python skills and experience with the scientific stack (NumPy, pandas, SciPy, scikit-learn or similar). You write clean, production-grade code, not just notebooks. Solid understanding of stochastic processes, Monte Carlo simulation, and time ...

Models (GPT, Claude), Generative AI, RAG Agentic AI, CoPilot, MCPs AIML Algorithms (Regression, Classification, Decision Trees, KNN, K-Means) Python (NLTK, NumPy, Scikit-learn, Pandas) Experience building AIML solutions for test generation, prioritization, defect triage, reporting, framework migration Strong understanding of QA automation frameworks and test lifecycle SQL proficiency Agile ...

data extraction and manipulation. Experience working with large datasets. Technical Skills Proficiency in cloud computing and python programming. Familiarity with Python libraries like Pandas, NumPy, scikit-learn. Experience with cloud services for mode training and deployment. Machine Learning Fundamentals Statistical concepts for robust data analysis. Linear algebra principles for modelling ...

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 ...

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 ...

timeseries based predictions. Proven expertise in time-series forecasting and spatio-temporal modeling, ideally applied to weather or energy systems. Strong proficiency in Python (Pandas, NumPy, PyTorch, TensorFlow, scikit-learn, Lightning, or equivalent). Solid understanding of data engineering and ML infrastructure Experience deploying ML models to cloud-native environments ...

cloud-native model training and deployment, model serving, and effective data lifecycle management. Expert proficiency in Python, SQL, and core ML libraries (e.g., Pandas, NumPy, Scikit-Learn, XGBoost). Strong experience working with cloud-based ML platforms (AWS preferred, e.g., SageMaker). Familiarity with model management and deployment tooling such ...