126 to 150 of 240 Pandas Jobs

types and structures Unit testing using pytest Proficiency in SQL (we use MySQL, but experience with other SQL platforms is welcome). Experience with data manipulation and transformation using Pandas . Familiarity with ETL/ELT processes and data warehousing concepts. Understanding of cloud platforms (AWS or Azure). Basic knowledge of Git and GitHub for version control and collaboration. More ❯

Ideally experience with de novo transcriptome assembly, Machine Learning and/or Artificial Intelligence, or experience mining genomic data Experience with Python and libraries such as SciPy, Pytorch, NumPy, Pandas etc. What Next? If you'd like to hear more about this exciting Bioinformatician position, just give me a call or drop me an email, I'd love to chat More ❯

degree in Computer Science (or related field). 3+ years of experience writing production-grade Python code. 3+ years of hands-on experience with core Python data libraries (e.g., pandas, numpy, sklearn, tensorflow, pytorch, matplotlib). At least 1 year of experience deploying machine learning models in production environments. 1-2 years of experience working with SQL/NoSQL databases More ❯

experience in data science or a quantitative academic field. Strong programming skills, with the ability to quickly become fluent in Python. Deep knowledge of core data science libraries (NumPy, Pandas, Scikit-Learn) and at least one deep learning framework (TensorFlow, PyTorch, or similar). High mathematical and statistical competence, with the ability to design new algorithms when needed. Experience leading More ❯

will then proceed to the Customer python screening, and the Customer ML interview. May not use AI LLMs during code screens. Candidates must have extreme fluency in numpy and pandas More ❯

accuracy. Requirements: 5+ years of experience applying data science in commercial settings Proven ability to lead data science projects from concept to production Strong Python skills (including libraries like Pandas, NumPy, Scikit-learn); experience with other languages is a plus Deep understanding of statistical modelling, predictive analytics, and optimisation techniques Comfortable working with cross-functional teams, including engineers, product leads More ❯

accuracy. Requirements: 5+ years of experience applying data science in commercial settings Proven ability to lead data science projects from concept to production Strong Python skills (including libraries like Pandas, NumPy, Scikit-learn); experience with other languages is a plus Deep understanding of statistical modelling, predictive analytics, and optimisation techniques Comfortable working with cross-functional teams, including engineers, product leads More ❯

Experience deploying and managing applications using Azure and Docker. Familiarity with frameworks such as LangChain and expertise in Retrieval-Augmented Generation (RAG) models for AI-driven applications. Proficiency with pandas for data manipulation. Full Stack Developer - What's in it for you? Salary Reviews: Twice a year to recognise your contributions. Generous Annual Leave: Enjoy 25 days plus three days More ❯

DB experience and Vector DB experience * Proven experience in time series analysis and forecasting, preferably in a commercial or industrial setting. * Strong proficiency in Python, including libraries such as Pandas, NumPy, and scikit-learn for data manipulation and modelling. * Experience with machine learning algorithms. * Proficient in data visualization techniques to effectively communicate insights from time series data. * Strong problem-solving More ❯

academic field Strong python programming skills as evidenced by earlier work in data science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

and performance of a potential solution Comprehensive knowledge of software engineering, programming fundamentals and industry experience using Python Experience with data manipulation and processing, such as SQL, Cypher or Pandas A can-do proactive and assertive attitude - your manager believes in freedom and responsibility and helping you own what you do; you will excel best if this environment suits you More ❯

and distribution of electrical energy) to secondary education minimum. - The ability to code in Python working with large datasets as a minimum. Familiarity with standard Python packages (NumPy/Pandas/Scikit-learn etc.) and some knowledge of VBA and SQL would also be preferable. - Competent user of specific Azure data related resources (or similar) including: Databricks and Storage Account More ❯

APIs, and managing databases. The requirements Essential: Python software engineering experience. SQL experience. Happy working in an unstructured, dynamic, and autonomous environment. Bonus: Mastery of SQL, Alchemy, scikit-learn, pandas, and PostgreSQL. Experience designing, building, and managing relational databases. Strong numerate background, i.e. maths, physics, comp-sci, engineering, etc. DevOps exposure: i.e. containerization, continuous integration/deployment (CI/CD More ❯

academic field Strong python programming skills as evidenced by earlier work in data science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

We are looking for individuals who have working experience in or are comfortable supporting (or willing to learn) in the following areas: Programming for Data Science (e.g., Python with Pandas, NumPy, R) Statistical Analysis and Hypothesis Testing Data Cleaning, Preprocessing, and Feature Engineering Data Visualization (e.g., Matplotlib, Seaborn, Plotly, Tableau) Machine Learning Fundamentals (e.g., Supervised, Unsupervised Learning) Machine Learning Algorithms More ❯

We are looking for individuals who have working experience in or are comfortable supporting (or willing to learn) in the following areas: Programming for Data Science (e.g., Python with Pandas, NumPy, R) Statistical Analysis and Hypothesis Testing Data Cleaning, Preprocessing, and Feature Engineering Data Visualization (e.g., Matplotlib, Seaborn, Plotly, Tableau) Machine Learning Fundamentals (e.g., Supervised, Unsupervised Learning) Machine Learning Algorithms More ❯

We are looking for individuals who have working experience in or are comfortable supporting (or willing to learn) in the following areas: Programming for Data Science (e.g., Python with Pandas, NumPy, R) Statistical Analysis and Hypothesis Testing Data Cleaning, Preprocessing, and Feature Engineering Data Visualization (e.g., Matplotlib, Seaborn, Plotly, Tableau) Machine Learning Fundamentals (e.g., Supervised, Unsupervised Learning) Machine Learning Algorithms More ❯

strong foundation in statistics, probability, and machine learning fundamentals either through a STEM degree, formal training, or self-study. Fluency in Python and SQL, including experience with libraries like Pandas, Scikit-learn, or equivalent. Demonstrated ability to solve real-world problems pragmatically using data. Clear, structured communication especially the ability to explain complex topics simply. A growth mindset: curious, driven More ❯

leadership or senior technical role. Proven experience in energy trading environments, particularly Natural Gas and Power markets. Expert in Python and SQL; strong experience with data engineering libraries (e.g., Pandas, PySpark, Dask). Deep knowledge of ETL/ELT frameworks and orchestration tools (e.g., Airflow, Azure Data Factory, Dagster). Proficient in cloud platforms (preferably Azure) and services such as More ❯

and implementing complex system integrations Experience with Python and .NET Experience in implementing data solutions adhering to regulatory requirements and security measures Experience with Python with libraries such as Pandas In-depth knowledge of various database system technologies (relational, document, columnar, etc.) Understanding and experience with relational databases and implementation of OLTP and OLAP systems Professional experience in data modeling More ❯

and successfully. You're confident in owning the process independently, from problem formulation to monitoring impact in production Technical Proficiency: Strong Python and experience with ML libraries (e.g., Scikitlearn, Pandas, LightGBM), plus working knowledge of cloud infrastructure and data tools (Snowflake, DBT, Omni) Structured Thinking: You use a clear and repeatable process to approach ML problems, including problem formulation, KPI More ❯

systems, with strong understanding of RAG workflows, including indexing, retrieval, and integration with language models. Technical Proficiency : Advanced proficiency in Python and experience with data manipulation and analysis libraries (Pandas, NumPy, SciPy), along with strong knowledge of machine learning algorithms, statistical modelling, and data mining techniques. Commercial Acumen : Deep understanding of product dynamics, market needs, and commercial considerations essential for More ❯

not important, we do require the ability to become a fluent Python programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

not important, we do require the ability to become a fluent Python programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of More ❯

and vice versa. Proven experience of change management skills. Core technical skills: Strong knowledge of data science fundamentals (Machine Learning methods, Statistics). Fluent in common analytics tools (Python, Pandas, Numpy, ScikitLearn, SQL, etc.) Comfortable to use data visualization libraries (e.g. Seaborn, Matplotlib) Demonstrated initiative, judgment and discretion while handling sensitive information Preferred Qualifications: If you have the following characteristics More ❯