1 to 25 of 79 Remote scikit-learn Jobs

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 ❯

Master’s degree from a 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 handling large datasets A passion More ❯

Master’s degree from a 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 handling large datasets A passion More ❯

ML models in production environments Continuously optimise models based on real-world feedback Maintain clear documentation for workflows and models Required Skills: Strong Python skills + ML libraries (scikit-learn, TensorFlow, PyTorch) Solid understanding of data preprocessing & feature engineering Knowledge of supervised, unsupervised, and deep learning methods Experience in model evaluation & tuning Familiarity with AWS, GCP, or Azure More ❯

to the team. Skills and Experience: Degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Proficiency in Python and key libraries such as NumPy, Pandas, scikit-learn, TensorFlow or PyTorch. Basic understanding of machine learning algorithms and model evaluation techniques. Strong analytical and communication skills. Comfortable working in a collaborative environment and taking feedback. Desirable More ❯

at scale. Deep familiarity with core ML concepts (classification, time-series, statistical modeling) and their real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from More ❯

to real-world use cases Contribute to code reviews, testing, and documentation Required Skills & Experience Strong proficiency in Python and its AI/ML ecosystem (e.g. NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow) Experience with machine learning model development , training, and deployment Familiarity with LLMs , NLP , or computer vision techniques Solid understanding of software engineering principles and version control More ❯

a compelling and actionable way Mentor junior data scientists and contribute to the evolution of data science standards and practices Key Skills & Technologies Technical: Strong in Python (pandas, scikit-learn, XGBoost, LightGBM, etc.) Proficient in SQL for complex customer data extraction and manipulation Experience with customer analytics techniques: segmentation, RFM analysis, clustering, time-series, A/B testing More ❯

related field. 5+ years' experience in data science, with proven success delivering end-to-end projects. Proficiency in Python, R, SQL, and machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Strong skills in data storytelling, problem-solving, and working with both structured and unstructured data. Experience with APIs, cloud platforms (especially AWS), and MLOps practices is a More ❯

from structured and unstructured datasets of varying sizes. Track record of implementing impactful models that drive sustained business results. Proficiency in the Python data science tech stack (pandas, scikit-learn, NumPy, and visualisation libraries) Experience working in a Linux-based cloud environment (e.g. GCP, Azure, AWS). Experience using git version control. Communication, stakeholder management, and problem-solving More ❯

degree in machine learning, computer science, engineering, or a related discipline, with a BSc required and an MSc considered advantageous. Experienced in using machine learning frameworks such as Scikit-learn, Keras, and PyTorch, with additional familiarity with MLFlow and AzureML seen as a positive. Have working knowledge of CI/CD practices, ML Ops, ML pipelines, automated testing More ❯

product and data teams Optimise model performance for accuracy, speed, and cost Run experiments, A/B tests, and validate approaches statistically Use tools like Python, TensorFlow, PyTorch, Scikit-learn, MLflow, SQL, Docker, AWS/GCP What Youll Need: Bachelors degree in Computer Science, AI, Machine Learning, or related field 1+ year of experience in a Machine Learning More ❯

product and data teams Optimise model performance for accuracy, speed, and cost Run experiments, A/B tests, and validate approaches statistically Use tools like Python, TensorFlow, PyTorch, Scikit-learn, MLflow, SQL, Docker, AWS/GCP What Youll Need: Bachelors degree in Computer Science, AI, Machine Learning, or related field 1+ year of experience in a Machine Learning More ❯

product and data teams Optimise model performance for accuracy, speed, and cost Run experiments, A/B tests, and validate approaches statistically Use tools like Python, TensorFlow, PyTorch, Scikit-learn, MLflow, SQL, Docker, AWS/GCP What Youll Need: Bachelors degree in Computer Science, AI, Machine Learning, or related field 1+ year of experience in a Machine Learning More ❯

implementing efficient data pipelines, including performance tuning and optimization. Proven ability to apply machine learning techniques to real-world problems. Familiarity with core libraries such as Pandas, NumPy, Scikit-learn, SciPy, and Polars is expected. Experience with digital signal processing, mobile sensor physics, or behavioural signal design. Proven track record of designing and delivering scalable data products, driving More ❯

collaborative, face-to-face, and iterative development environment. Skills & Experience Strong foundation in applied mathematics, statistics, or machine learning. Experience building and deploying models in Python (pandas, NumPy, scikit-learn, etc.). Comfort working with large and complex datasets, including data wrangling and exploratory analysis. Experience (or strong interest) in GPU-powered model development (e.g. using PyTorch, TensorFlow More ❯

collaborative, face-to-face, and iterative development environment. Skills & Experience Strong foundation in applied mathematics, statistics, or machine learning. Experience building and deploying models in Python (pandas, NumPy, scikit-learn, etc.). Comfort working with large and complex datasets, including data wrangling and exploratory analysis. Experience (or strong interest) in GPU-powered model development (e.g. using PyTorch, TensorFlow More ❯

data science or data engineering solutions into production. You're comfortable writing production-grade code, not just notebooks. Strong Python and SQL skills, including the basic libraries (Pandas, Numpy, ScikitLearn). You value writing clean, maintainable, and tested code. Proven ability to design, build, and maintain data pipelines and tools from scratch that are reliable, maintainable, and scalable. Strong communicator More ❯

new. You should have: 2+ years of experience in machine learning or data science roles Strong programming skills in Python and experience with ML libraries (e.g., TensorFlow, PyTorch, scikit-learn) Solid understanding of machine learning algorithms and statistical methods Experience working with cloud platforms (AWS, GCP, or Azure) Familiarity with MLOps tools and practices is a plus A More ❯

Engineering, Mathematics, or related field with relevant coursework in machine learning/statistics, software engineering principles, and database systems Core Skills: Python, ML frameworks (TensorFlow/PyTorch/scikit-learn), SQL, distributed computing, version control Experience: 1+ years in ML engineering or data engineering Mindset: Self-motivated with a growth mindset, adaptable to fast-paced startup environment, comfortable More ❯

Engineering, Mathematics, or related field with relevant coursework in machine learning/statistics, software engineering principles, and database systems Core Skills: Python, ML frameworks (TensorFlow/PyTorch/scikit-learn), SQL, distributed computing, version control Experience: 1+ years in ML engineering or data engineering Mindset: Self-motivated with a growth mindset, adaptable to fast-paced startup environment, comfortable More ❯

leadership or architecture role, ideally with some experience in leading a Centre of Excellence or a similar initiative. • Hands-on experience with Al frameworks such as TensorFlow, PyTorch, Scikit-learn, and cloud platforms like AWS, Azure, or Google Cloud. • Experience in multiple industries is advantageous (e.g., healthcare, finance, retail). Skills: AI/ML Expertise: Strong understanding of More ❯

Logistic Regression, Random Forest, XGBoost, etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Strong knowledge of SQL and Python's ecosystem for data analysis (Jupyter, Pandas, Scikit Learn, Matplotlib) Strong software development skills (Python is the preferred language) Proven experience in deploying ML/AI services suing Kubernetes & KubeFlow Strong management and leadership skills - previous experience More ❯

data pipeline orchestration and a strong grasp of DevSecOps best practices in cloud-native environments • Hands-on experience with key tools and frameworks, including: o Python, NumPy, Pandas, scikit-learn o TensorFlow or PyTorch o Elasticsearch, Logstash, Kibana • Preferred Experience in GDS (Government Digital Services) or USDS (U.S. Digital Services) Education Requirement • Shall have at a minimum, a More ❯

senior data science or quantitative academic position Strong Python programming skills, with the ability to write production-grade code Proficiency in core data science libraries (e.g. NumPy, Pandas, Scikit-learn) and familiarity with at least one deep learning framework (e.g. TensorFlow, PyTorch) A solid understanding of the full range of data science techniques, including supervised and unsupervised learning More ❯