1 to 25 of 1,194 scikit-learn Jobs

Computer Science, Statistics, Mathematics, or related fields (PhD a plus) Strong programming skills in Python, with proficiency in data science libraries (NumPy, Pandas, scikit-learn) Experience with SQL for data querying and analysis Solid understanding of machine learning algorithms, statistical methods, and predictive modeling Experience with NLP techniques More ❯

Advanced Programming Skills: Python: Mastery of Python for machine learning, data manipulation, and automation, with extensive use of libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch. SQL : Advanced knowledge of SQL for manipulating and querying data, feature generation and functions (in particular Snowflake’s SQL dialect More ❯

5+ years of experience in machine learning, deep learning, or AI engineering Strong proficiency in Python and AI/ML frameworks (TensorFlow, PyTorch, scikit-learn) Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes) Solid understanding of ML fundamentals including supervised/unsupervised learning More ❯

expertise and work experience with ML projects, both supervised and unsupervised. Proficient programming skills with Python, including libraries such as NumPy, pandas, and scikit-learn, as well as R. Understanding and usage of the OpenAI API. NLP: tokenization, embeddings, sentiment analysis, basic transformers for text-heavy datasets. Experience More ❯

expertise and work experience with ML projects, both supervised and unsupervised. Proficient programming skills with Python, including libraries such as NumPy, pandas, and scikit-learn, as well as R. Understanding and usage of the OpenAI API. NLP: tokenization, embeddings, sentiment analysis, basic transformers for text-heavy datasets. Experience More ❯

data science, with strong proficiency in writing scale and production-grade code in Python. Solid understanding and proficiency in working with Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch, SQL. Proficiency in advanced data visualization tools and libraries (e.g., Matplotlib, Seaborn, Plotly, Tableau) for creating insightful and interactive visualizations. More ❯

data science, with strong proficiency in writing scale and production-grade code in Python. Solid understanding and proficiency in working with Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch, SQL. Proficiency in advanced data visualization tools and libraries (e.g., Matplotlib, Seaborn, Plotly, Tableau) for creating insightful and interactive visualizations. More ❯

Russell Group university in Data Science, Computer Science, Mathematics, Physics, Engineering, or a related discipline Strong programming skills in Python (e.g. NumPy, pandas, scikit-learn, matplotlib); R is also welcome A solid understanding of core machine learning concepts, data wrangling, and model evaluation Proficiency with SQL and experience More ❯

Russell Group university in Data Science, Computer Science, Mathematics, Physics, Engineering, or a related discipline Strong programming skills in Python (e.g. NumPy, pandas, scikit-learn, matplotlib); R is also welcome A solid understanding of core machine learning concepts, data wrangling, and model evaluation Proficiency with SQL and experience More ❯

techniques, including LLMs, GenAI, and automated AI systems. • Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and data science libraries (e.g., NumPy, pandas, scikit-learn). • Proficiency in Python, R, or other relevant programming languages. • Proficiency in working with large datasets, data wrangling, and data preprocessing. • Experience in More ❯

and finance/savings data (transaction flows, compliance, user segmentation, etc.) Technical Skills: Proficient in Python, SQL, and data science libraries (Pandas, NumPy, Scikit-learn, Hugging Face Transformers) Familiarity with embedding models, vector databases (e.g., Pinecone, FAISS, Weaviate) Experience with cloud platforms (AWS, GCP, or Azure) and MLOps More ❯

development and data analytics • Strong programming skills in Python (preferred), R, or similar languages • Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn • Experience working with and customizing LLMs and transformer-based architectures • Proficiency in data wrangling, data cleaning, and exploratory data analysis • Familiarity with cloud More ❯

learn and adapt to new tools and technologies. Preferred Qualifications (Nice-to-Have): Basic understanding of machine learning concepts and frameworks like Scikit-learn, TensorFlow, or PyTorch. Understanding of DevOps practices and CI/CD pipelines. Familiarity with cloud services such as AWS, Azure, or GCP. Hands More ❯

learn and adapt to new tools and technologies. Preferred Qualifications (Nice-to-Have): Basic understanding of machine learning concepts and frameworks like Scikit-learn, TensorFlow, or PyTorch . Understanding of DevOps practices and CI/CD pipelines. Familiarity with cloud services such as AWS, Azure, or GCP. More ❯

learn and adapt to new tools and technologies. Preferred Qualifications (Nice-to-Have) Basic understanding of machine learning concepts and frameworks like Scikit-learn, TensorFlow, or PyTorch . Understanding of DevOps practices and CI/CD pipelines. Familiarity with cloud services such as AWS, Azure, or GCP. More ❯

techniques, including LLMs, GenAI, and automated AI systems. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and data science libraries (e.g., NumPy, pandas, scikit-learn). Proficiency in Python, R, or other relevant programming languages. Proficiency in working with large datasets, data wrangling, and data preprocessing. Experience in More ❯

Statistics, or Physics 3-7 years of relevant experience in a data science/ML engineering role Expert in Python and key libraries (Scikit-learn, Pandas, PyTorch, TensorFlow, XGBoost, Transformers) Strong understanding of statistics, ML algorithms, and data wrangling Familiarity with cloud platforms (Azure, AWS, GCP) and containerized More ❯

methods (SHAP, LIME) and adopt best practices for responsible AI. Qualifications Technical Skills Programming: Python (NumPy, Pandas), R, SQL. ML/DL Frameworks: Scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers. Big Data & Cloud: Databricks, Azure ML, AWS SageMaker, GCP Vertex AI. Automation: MLflow, Kubeflow, Weights & Biases for experiment More ❯

C, Julia, C++, or similar. • Strong proficiency in Python and its scientific computing/ML ecosystem (e.g., NumPy, Pandas, TensorFlow, AI Foundry, OpenAI, Scikit-learn etc.). • Solid understanding of core machine learning concepts • Hands-on experience with at least one major deep learning framework (e.g., TensorFlow, PyTorch More ❯

a production environment. Proficiency in programming languages such as Python, Java, or C++. Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Familiarity with data processing tools and platforms (e.g., SQL, Apache Spark, Hadoop). Knowledge of cloud computing services (e.g., AWS, Google Cloud More ❯

and PyTorch. Exposure to LLMs from model families such as Anthropic, Meta, Amazon, and OpenAI. Familiarity with tools and packages like Pandas, NumPy, scikit-learn, Plotly/Matplotlib, and Jupyter Notebooks. Knowledge of ML-adjacent technologies, including AWS SageMaker and Apache Airflow. Proficiency in data pre-processing, data More ❯

or a related field 5+ years of experience in AI and machine learning and deep learning model development/frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost Strong programming skills in languages such as Python, R, Java, C++, and/or MATLAB Knowledge, Skill and Abilities: Proven experience utilizing More ❯

GenAI projects and related frameworks (RAG apps, vector DBs, LangChain, LlamaIndex, agentic frameworks, ...) Advanced knowledge of Python and machine learning frameworks (SciPy, Scikit-learn, TensorFlow, PyTorch, pyMC, pgmpy, ...) Hands-on experience with one or more cloud computing platforms (Azure - preferred, AWS, GCP). Understanding of the More ❯

machine learning. Hands-on experience with Python and deep learning frameworks like TensorFlow, Keras, PyTorch, MXNet. Knowledge of machine learning libraries such as scikit-learn, NumPy, Pandas, MLlib. Strong data engineering, communication, and presentation skills. PhD or Master's in computer science, engineering, mathematics, or a related quantitative More ❯

Selenium). Strong knowledge of data cleaning, standardization, and normalization techniques Experience with data analysis and modeling using libraries such as Pandas, NumPy, Scikit-learn, or TensorFlow. Familiarity with SQL and database management systems (e.g., PostgreSQL, MySQL). Experience with cloud platforms (e.g., AWS, Azure, GCP) and big More ❯