51 to 75 of 1,275 scikit-learn Jobs

in creating and optimizing machine learning models, generative AI, and time series analysis. Mastery of Python and/or R for data analysis (scikit-learn, TensorFlow, PyTorch? You've got it). Strong SQL/NoSQL skills - you can navigate databases like a pro. Bonus Points: Experience working More ❯

and Python. An ability to analyse complex datasets and extract actionable insights. Experience with machine learning and deep learning frameworks (e.g. TensorFlow, PyTorch, Scikit-learn). Skilled in building credible statistical models. Knowledge of various learning algorithms (supervised, unsupervised, reinforcement). Experience with MLOps practices. Demonstrable experience building More ❯

in NLP or classification problems. Hands on experience with ML tools like TensorFlow, PyTorch etc. Experience with data science libraries such as NLTK, Scikit-learn, SciPy, (Sci)SpaCy etc. Excellent problem-solving and programming skills in Python Excellent communication skills Preferred Qualifications & Skills: If you have the following More ❯

Expertise Strong experience in machine learning, deep learning, and statistical analysis. Expertise in Python, with proficiency in ML and NLP libraries such as Scikit-learn, TensorFlow, Faiss, LangChain, Transformers and PyTorch. Experience with big data tools such as Hadoop, Spark, and Hive. Familiarity with CI/CD and More ❯

knowledge of standard machine learning algorithms and statistical methods Proficient in python and its standard data science and ML libraries (e.g., numpy, pandas, scikit-learn, boosting and data visualization libraries) Practical experience in data processing and feature engineering for machine learning applications Ability to write robust production-ready More ❯

or causal inference Proficiency in Python for data analysis and statistical modeling (pandas, numpy, scipy, statsmodels) Familiarity with machine learning libraries such as scikit-learn Experience with SQL for data extraction and manipulation Demonstrated ability to translate analytical findings into business recommendations Track record of successful project delivery More ❯

and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, NLTK). Experience with NLP techniques, such as named entity recognition (NER), topic modeling, semantic similarity, and knowledge graph construction. Demonstrated ability to More ❯

and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, NLTK). Experience with NLP techniques, such as named entity recognition (NER), topic modeling, semantic similarity, and knowledge graph construction. Demonstrated ability to More ❯

production ready code, architect packages, and make sound architectural and procedural choices for effective shared codebases Python and scientific computing packages (Pytorch, Numpy, Scikit-learn, TensorFlow) Database technologies such as ElasticSearch, Neo4j, and SQL Excellent interpersonal, verbal, and written communication skills A flexible attitude with respect to work More ❯

life science organization is considered an asset. Other skills we are searching for are: Programming Skills: Proficiency in Python, data analytics, deep learning (Scikit-learn, Pandas, PyTorch, Jupyter, pipelines), and practical knowledge of data tools like Databricks, Ray, Vector Databases, Kubernetes, and workflow scheduling tools such as Apache More ❯

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 More ❯

Strong grasp of ML/AI algorithms, including LLMs and automated AI systems - Proficient in Python or R and key frameworks (TensorFlow, PyTorch, scikit-learn) - Skilled in handling large datasets, data wrangling, and statistical modelling - Comfortable working independently on end-to-end ML pipelines - Experienced in visualisation tools More ❯

years of experience as a Data Scientist or related position. Also requires 3 years of experience with Python and the following libraries: Pandas, Scikit-learn, Scipy, TensorFlow. 3 years of experience working with Java and Spring Framework. 3 years of experience in machine learning and deep learning algorithms More ❯

as an AI or Machine Learning Engineer in a freelance or contract role Proficiency in Python and relevant ML libraries (e.g. TensorFlow, PyTorch, Scikit-learn) Experience with model deployment tools and APIs (e.g. Docker, FastAPI, MLflow) Solid understanding of data processing tools (Pandas, Spark, SQL, etc.) Familiar with More ❯

as an AI or Machine Learning Engineer in a freelance or contract role Proficiency in Python and relevant ML libraries (e.g. TensorFlow, PyTorch, Scikit-learn) Experience with model deployment tools and APIs (e.g. Docker, FastAPI, MLflow) Solid understanding of data processing tools (Pandas, Spark, SQL, etc.) Familiar with More ❯

practices Hands-on experience with cloud services (Azure ML, AWS SageMaker, GCP Vertex AI) Proficient in Python and common ML libraries (TensorFlow, PyTorch, Scikit-learn) Familiarity with data engineering workflows and tools (Spark, Airflow, Databricks, etc.) Experience with GenAI, LLMs, or NLP is a strong plus Solid understanding More ❯

in Computer Science, Mathematics, Statistics, or a related field. Solid understanding of probability, statistics, and linear algebra. Proficiency in Python, including libraries like scikit-learn, pandas, seaborn, and matplotlib. Experience writing and optimising SQL queries for data retrieval and manipulation. Ability to communicate complex insights effectively. Detail-oriented More ❯

or similar Python frameworks like Streamlit) for dashboard creation. Experience with Machine Learning model development and data science workflows (including frameworks such as scikit-learn, PyTorch, or TensorFlow). Experience in Quantitative Finance or strong interest in mathematical/financial modeling, derivatives pricing, or algorithmic trading. Familiarity with More ❯

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 More ❯

modeling techniques, and data mining principles. Proficiency in programming languages such as Python or R, and experience with data science libraries (e.g., Pandas, Scikit-learn, TensorFlow). Experience with data visualization tools such as Tableau or Power BI. Familiarity with Agile methodologies and MBSE principles is a plus. More ❯

services or wealth management. Proficiency in programming languages such as Python, R, or Java, and familiarity with data analysis libraries (e.g., Pandas, NumPy, scikit-learn). Strong understanding of database technologies and SQL proficiency for data manipulation and querying. Excellent analytical, statistical, and problem-solving capabilities. Ability to More ❯

equivalent qualification, or other evidence of equivalent knowledge. Professional or research experience in some of the following: ML/NLP tools like PyTorch, Scikit-learn, Transformer architecture and Neural Networks. Coding and software engineering in mainstream programming languages, such as Python. Statistical packages for data manipulation and in More ❯

in data science, machine learning, or a related role Proficiency in programming languages such as Python or R, and libraries like TensorFlow, PyTorch, Scikit-learn, or Pandas Strong understanding of statistical modeling, probability, and optimization techniques Ability to work with messy, real-world datasets and perform feature engineering More ❯

equivalent qualification, or other evidence of equivalent knowledge. Professional or research experience in some of the following: ML/NLP tools like PyTorch, Scikit-learn, Transformer architecture, and Neural Networks. Coding and software engineering in mainstream programming languages, such as Python. Statistical packages for data manipulation and in More ❯

Spark. At least 2 years' experience with machine learning, deep learning, or AI systems. Familiarity with key libraries and frameworks such as TensorFlow, Scikit-learn, PyTorch, Transformers, LangChain, BeautifulSoup, and OpenCV. Experience with AI APIs and platforms (e.g., OpenAI, Bedrock, Gemini, Anthropic). Knowledge of cloud infrastructure (ideally More ❯