101 to 125 of 178 Permanent scikit-learn Jobs

research PhD, or a Master's degree and experience building machine learning models or developing algorithms for business application Preferred Qualifications Experience with modeling tools such as R, scikit learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy, etc. Experience with large scale distributed systems such as Hadoop, Spark, etc. Amazon is an equal opportunities employer. We believe passionately that More ❯

What We're Looking For A Bachelor's degree in Computer Science, Mathematics, Electrical Engineering, or a related field. Strong experience with Python and data science libraries (Pandas, Scikit-learn, etc.). Solid understanding of machine learning concepts and algorithms . Interest in working with real-world industrial or sensor data . Exposure to Apache Airflow and/ More ❯

business narratives Contributing to simple, reliable data pipelines and automated quality checks About You Minimum 1 year of commercial Data Science experience Strong hands-on Python (pandas, NumPy, scikit-learn) and SQL Sound understanding of statistics and forecasting Confident communicator - able to explain technical work clearly to non-technical audiences Why Apply Opportunity to build from scratch within More ❯

a senior data science role. Deep understanding of machine learning model lifecycles and optimisation. Skilled in building, training, and deploying models on large datasets. Proficiency in Python, PyTorch, Scikit-learn, and similar ML frameworks. Experience with cloud environments (AWS preferred), containerisation, and modern data infrastructure. Excellent communication skills, with the ability to work effectively across technical and non More ❯

a senior data science role. Deep understanding of machine learning model lifecycles and optimisation. Skilled in building, training, and deploying models on large datasets. Proficiency in Python, PyTorch, Scikit-learn, and similar ML frameworks. Experience with cloud environments (AWS preferred), containerisation, and modern data infrastructure. Excellent communication skills, with the ability to work effectively across technical and non More ❯

in the job offered or a related position. Experience must include demonstrable knowledge of: deep learning models for segmentation/classification using medical imaging; Python; SQL; typescript; PyTorch; scikit-learn; OpenCV; PIL; pydicom; matplotlib; seaborn; SHAP; Grad-CAM; AWS; Fast API; Kubernetes; PostgreSQL; Snowflake; MongoDB; OpenAI API; Git; GitHub; Slack, and SaMD principles. Required knowledge may be acquired More ❯

strong problem-solving skills, a quick learning ability, and enthusiasm for tackling complex challenges. You are proficient in Python, with experience using PySpark and ML libraries such as scikit-learn, TensorFlow, or Keras . You are familiar with big data technologies (e.g., Hadoop, Spark), cloud platforms (AWS, GCP), and can effectively communicate technical concepts to non-technical stakeholders. More ❯

Python or related language - Experience with neural deep learning methods and machine learning - Experience with prompting techniques for LLMs PREFERRED QUALIFICATIONS - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. - Experience with large scale distributed systems such as Hadoop, Spark etc. - PhD in math/statistics/engineering or other equivalent More ❯

Vertex AI, Cloud Run) Strong SQL complex querying Python for analytics, backend logic, and model prototyping Familiarity with LLM APIs , prompt engineering , embeddings , and traditional ML (e.g. XGBoost , scikit-learn ) Comfortable deploying tools using Docker , Flask/FastAPI , and GCP services Ability to work independently and iterate quickly toward high-quality outcomes Full-stack data capability, from pipelines More ❯

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 practical More ❯

NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant tools & mathematical background • Docker, Kubernetes, AWS 📍 Location : London (1 remote day per week) 💼 Type : Full-time | Competitive comp + performance bonus Ready to build systems that More ❯

NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant tools & mathematical background • Docker, Kubernetes, AWS 📍 Location : London (1 remote day per week) 💼 Type : Full-time | Competitive comp + performance bonus Ready to build systems that More ❯

NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant tools & mathematical background • Docker, Kubernetes, AWS 📍 Location : London (1 remote day per week) 💼 Type : Full-time | Competitive comp + performance bonus Ready to build systems that More ❯

NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant tools & mathematical background • Docker, Kubernetes, AWS 📍 Location : London (1 remote day per week) 💼 Type : Full-time | Competitive comp + performance bonus Ready to build systems that More ❯

NumPy, Pandas, SciPy) • SQL or time-series DBs • Linux & distributed systems expertise • Strong communication & market intuition ✨ Bonus Skills • KDB+/Q, Haskell, or other functional languages • Machine learning (scikit-learn, etc.) • Quant tools & mathematical background • Docker, Kubernetes, AWS 📍 Location : London (1 remote day per week) 💼 Type : Full-time | Competitive comp + performance bonus Ready to build systems that More ❯

years' experience in applied machine learning and generative AI, including work with large language models. Strong Python programming skills with experience in core ML libraries (numpy, pandas, scikit-learn, boosting methods). Proven ability to design, test, and deploy production-ready machine learning solutions. Hands-on experience in data processing and feature engineering for complex datasets. A degree More ❯

to stochastic optimisation models. What skills, experience and qualities are we looking for? Python - demonstrable experience required Experience working across a range of ML models, e.g. TensorFlow and scikit-learn Significant experience in the energy industry, with a specific focus on forecasting the short-term power markets GitHub or Azure DevOps knowledge is desired SQL knowledge is desired More ❯

evaluation Experience in SQL and Python for advance analytics and modelling (experience with Snowflake, R, GitHub, and Jira is a plus) Experience using Python libraries such as pandas, scikit-learn, and statsmodels (or R equivalent) Experience using BI tools like Power BI or Tableau to communicate insights Experience mentoring or upskilling colleagues in analytics tools (such as SQL More ❯

evaluation Experience in SQL and Python for advance analytics and modelling (experience with Snowflake, R, GitHub, and Jira is a plus) Experience using Python libraries such as pandas, scikit-learn, and statsmodels (or R equivalent) Experience using BI tools like Power BI or Tableau to communicate insights Experience mentoring or upskilling colleagues in analytics tools (such as SQL More ❯

knowledge of running cost-effective serverless architecture. Experience working with Python, C#, and Angular. Strong interpersonal, communication, and presentation skills applicable to a wide audience. Experience with PyTorch, Scikit-learn, Go, Databricks, JavaScript, and Azure Pipelines is desirable, but not essential. Experience in leading software engineering efforts for AI-enabled SaaS products is desirable, but not essential. Why More ❯

experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning PREFERRED QUALIFICATIONS - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. - Experience with large scale distributed systems such as Hadoop, Spark etc. Amazon is an equal opportunity employer and does not discriminate More ❯

or desirable in a model. Familiarity with Python and the packages widely used in data science and machine learning. Developers should be familiar with libraries like NumPy, pandas, scikit-learn, TensorFlow, and PyTorch. Familiarity with best practice in Machine Learning workflows and MLOps tools. Fluency in validation and evaluation framework and metrics frameworks for machine learning such as More ❯

or desirable in a model. Familiarity with Python and the packages widely used in data science and machine learning. Developers should be familiar with libraries like NumPy, pandas, scikit-learn, TensorFlow, and PyTorch. Familiarity with best practice in Machine Learning workflows and MLOps tools. Fluency in validation and evaluation framework and metrics frameworks for machine learning such as More ❯

or desirable in a model. Familiarity with Python and the packages widely used in data science and machine learning. Developers should be familiar with libraries like NumPy, pandas, scikit-learn, TensorFlow, and PyTorch. Familiarity with best practice in Machine Learning workflows and MLOps tools. Fluency in validation and evaluation framework and metrics frameworks for machine learning such as More ❯

or desirable in a model. Familiarity with Python and the packages widely used in data science and machine learning. Developers should be familiar with libraries like NumPy, pandas, scikit-learn, TensorFlow, and PyTorch. Familiarity with best practice in Machine Learning workflows and MLOps tools. Fluency in validation and evaluation framework and metrics frameworks for machine learning such as More ❯