1 to 25 of 130 Pandas Jobs

Engineering, AI, or related field. 7+ years of professional software development experience, with at least 3 years in AI/ML. Strong proficiency in Python , including libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch . Solid understanding of ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . More ❯

control (Git), and Agile methodologies.Excellent analytical, problem-solving, and communication skills.Preferred SkillsExperience with data engineering, ETL workflows, or big data frameworks (Spark, Airflow).Knowledge of machine learning libraries (NumPy, Pandas, Scikit-learn, TensorFlow, etc.) is a plus.Exposure to DevOps practices, infrastructure as code, and monitoring tools (Jenkins, Terraform, Prometheus).Familiarity with security best practices for Python-based applications.Prior experience in More ❯

PostgreSQL, SQL Server, Snowflake, Redshift, Presto, etc Experience building ETL and stream processing pipelines using Kafka, Spark, Flink, Airflow/Prefect, etc. Familiarity with data science stack: e.g. Juypter, Pandas, Scikit-learn, Dask, Pytorch, MLFlow, Kubeflow, etc. Strong experience with using AWS/Google Cloud Platform (S3S, EC2E, IAM, etc, Kubernetes and Linux in production Strong proclivity for automation and More ❯

Git), and Agile methodologies.Excellent analytical, problem-solving, and communication skills. Preferred Skills Experience with data engineering, ETL workflows, or big data frameworks (Spark, AirflowKnowledge of machine learning libraries (NumPy, Pandas, Scikit-learn, TensorFlow, etc is a plus.Exposure to DevOps practices, infrastructure as code, and monitoring tools (Jenkins, Terraform, PrometheusFamiliarity with security best practices for Python-based applications.Prior experience in domains More ❯

framework experience (SQLAlchemy) Containerization familiarity with Docker and/or Kubernetes Cloud platform knowledge (AWS preferred, but also Azure, GCP) Plusses: Experience working with data scientists Data library experience (Pandas and/or NumPy) Knowledge of microservices architecture and RESTful API design Integration experience with LangChain or similar AI frameworks to build AI based workflows Technical - python, containerization, cloud platform More ❯

and more on reliability and fixes. Key Skills: Investigating and debugging complex data flow and Machine Learning issues within a live, high impact production environment. Extensive Python, NumPy and Pandas is required for this role. You must demonstrate a deep commercial background in the following areas: Extensive Python: Very strong, production-level Python coding and debugging skills. Production Environment: Proven More ❯

design) Strong background in AI/ML with experience using frameworks such as TensorFlow, PyTorch, or Scikit-learn Proficiency in data handling and manipulation using libraries like NumPy and Pandas Experience with SQL databases for managing and accessing training data Knowledge of model deployment and scaling in enterprise or cloud environments (AWS, Azure, or GCP) Familiarity with containerization and orchestration More ❯

alignment between technology initiatives and business objectives. Required Qualifications: 6+ years of experience in full stack software development. Proficiency in sever side Python programming. Proficiency in data analysis using Pandas, Numpy, SciPy etc. Experience with object oriented design, distributed systems architecture, performance tuning. Experience with designing and programming relational database such as MySQL, RedShift, Oracle SQL Server, or Postgres. Experience More ❯

matter expert for pricing models and valuation logic, supporting risk and trading teams globally. Skills and Experience Expert-level Python developer with strong experience in numerical computing (NumPy, SciPy, Pandas). Deep understanding of derivatives pricing theory, volatility modelling, and stochastic calculus. Experience with calibration, curve bootstrapping, and risk measures (Greeks, sensitivities, VaR). Background in pricing and risk models More ❯

a thought leader in data science. Requirements 5+ years of experience in data science, machine learning, or AI model development. Expertise in Python, R, or Julia, with proficiency in pandas, NumPy, SciPy, scikit-learn, TensorFlow, or PyTorch. Experience with SQL, NoSQL, and big data technologies (Spark, Hadoop, Snowflake, Databricks, etc.). Strong background in statistical modelling, probability theory, and mathematical More ❯

end data science projects from ideation to delivery, including business scoping and stakeholder management. Strong proficiency in Python (or R), with deep experience using modern data science libraries (e.g., Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Statsmodels). Solid foundation in SQL and data wrangling across large, complex datasets. Hands-on experience with experimentation platforms, data visualization, and dashboarding tools (e.g. More ❯

AI/ML development. Strong proficiency in Python, R, or Java. Experience with machine learning libraries such as TensorFlow, Keras, or Scikit-learn. Familiarity with data processing tools (e.g., Pandas, NumPy). Knowledge of AI model deployment and cloud services (AWS, Google Cloud, Azure). Solid understanding of algorithms and data structures. Excellent analytical skills and problem-solving capability. Strong More ❯

AI/ML development. Strong proficiency in Python, R, or Java. Experience with machine learning libraries such as TensorFlow, Keras, or Scikit-learn. Familiarity with data processing tools (e.g., Pandas, NumPy). Knowledge of AI model deployment and cloud services (AWS, Google Cloud, Azure). Solid understanding of algorithms and data structures. Excellent analytical skills and problem-solving capability. Strong More ❯

AI/ML development. Strong proficiency in Python, R, or Java. Experience with machine learning libraries such as TensorFlow, Keras, or Scikit-learn. Familiarity with data processing tools (e.g., Pandas, NumPy). Knowledge of AI model deployment and cloud services (AWS, Google Cloud, Azure). Solid understanding of algorithms and data structures. Excellent analytical skills and problem-solving capability. Strong More ❯

Manage cloud-based and on-prem setup Requirements: 25 years experience in ML engineering/MLOps roles Strong proficiency in Python and experience with relevant ML libraries/frameworks (pandas, numpy, sklearn, TensorFlow, PyTorch) Strong understanding and experience in implementing end-to-end ML pipelines (data, training, validation, serving) Experience with ML workflow orchestration tools (e.g., Airflow, Prefect, Kubeflow) and More ❯

continuous improvement initiatives Essential Skills for the AWS 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 SQL and working More ❯

and causal inference models, preferably in pricing, marketplace, or supply chain contexts. Experience with experimental design and statistical inference in real-world business settings. Technical Skills: Proficiency in Python (pandas, NumPy, SciPy, scikit-learn, TensorFlow/PyTorch preferred). Strong SQL skills and experience querying large-scale data platforms (e.g., Snowflake, Redshift). Familiarity with scientific software principles (version control More ❯

development, including: - Implementation of AI functionality - RAG, MCP, Agentic AI - Familiarity with ML concepts, techniques, and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, Keras) - Experience with data handling libraries (NumPy, Pandas) and AI/ML data pipelines - Experience designing and implementing data pipelines (batch and streaming) feeding into AI/ML tools Proven experience developing large-scale applications using Java (Spring More ❯

learning fundamentals, and experimental design. Experience with predictive modeling techniques such as regression, classification, clustering, or time-series forecasting. Proficiency in Python and experience with data science libraries (e.g., Pandas, NumPy, scikit-learn, XGBoost, PyTorch, TensorFlow). Strong experience with SQL and data manipulation across large datasets. Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Plotly, Tableau, or Power BI More ❯

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 ❯

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 ❯

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 ❯