1 to 25 of 211 scikit-learn Jobs

experimental design. Experience with predictive modeling techniques such as regression, classification, clustering, or time-series forecasting. Proficiency in Python and experience with data science libraries (e.g., Pandas, NumPy, scikit-learn, XGBoost, PyTorch, TensorFlow). Strong experience with SQL and data manipulation across large datasets. Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Plotly, Tableau, or Power BI). More ❯

projects from ideation to delivery, including business scoping and stakeholder management. Strong proficiency in Python (or R), with deep experience using modern data science libraries (e.g., Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Statsmodels). Solid foundation in SQL and data wrangling across large, complex datasets. Hands-on experience with experimentation platforms, data visualization, and dashboarding tools (e.g., Tableau More ❯

AI, with a strong portfolio of high-impact projects in production Expert-level programming skills in Python and SQL, and fluency with leading ML/AI frameworks (e.g., scikit-learn, TensorFlow, PyTorch) Direct experience with GenAI/LLM technologies, including tools like Hugging Face, LangChain, OpenAI APIs, vector databases, and fine-tuning methods Deep knowledge of machine learning More ❯

business adoption of AI/ML practices. Qualifications Advanced proficiency in Python (specifically for machine learning) and extensive experience with core AI/ML open-source libraries, including scikit-learn, PyTorch, pandas, polars, NumPy, and seaborn. Proven experience designing and deploying end-to-end AI/ML systems, with a strong emphasis on MLOps principles and tools (Docker More ❯

concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering More ❯

e.g., Pandas, Polars, NumPy). Hands-on experience in building and maintaining automated data pipelines for large-scale data processing. Familiarity with machine learning frameworks (e.g., PyTorch, JAX, scikit-learn) as applied to data quality and augmentation tasks. Expertise in working with healthcare data, including familiarity with the OMOP Common Data Model (OMOP CDM). Strong experience in More ❯

developments in AI, machine learning, and data science methodologies. Experienced Needed: Masters or PhD in a STEM subject Proficiency in Python, with experience in libraries such as pandas, scikit-learn, TensorFlow, or PyTorch. Solid SQL skills and experience working with relational databases. Exposure to cloud platforms (AWS, GCP, or Azure) would be advantageous. Strong analytical and problem-solving More ❯

solutions. Optimize models for scalability, performance, and accuracy. Mentor junior engineers and review code for quality and best practices. Required Skills & Experience Strong proficiency in Python (Pandas, NumPy, Scikit-learn, FastAPI/Flask). Experience with machine learning workflows: data preprocessing, model training, evaluation, and deployment preferably using Microsoft stack - Azure ML, Azure Data Factory, Synapse Analytics, and More ❯

techniques (e.g., deep learning, reinforcement learning, generative models). Support continuous model improvement and scalable MLOps deployment pipelines. TECH STACK/REQUIREMENTS Core Skills: Python, TensorFlow/PyTorch, scikit-learn, OpenCV, NumPy, Pandas Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP More ❯

AI workflows, models, and system architecture. Skills & Qualifications Proficiency in programming languages such as Python, Java, or C++. Strong understanding of machine learning frameworks (eg, PyTorch (preferred), TensorFlow, Scikit-learn). Experience with data processing tools and cloud platforms (eg, Azure, GCP, AWS). Knowledge of deep learning, NLP, and computer vision techniques, including experience with Microsoft Copilot More ❯

techniques (supervised and unsupervised learning, natural language processing, Bayesian statistics, time-series forecasting, collaborative filtering etc) ? Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) ? Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku, Databricks. ? Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. ? Ability to work More ❯

Data, Tools Development Proficiency in Python, with a background in back end services and data processing Exposure to AI/ML algorithms Familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn) Understanding of LLMs, vector databases, and retrieval systems Experience with Model Context Protocol (MCP) integration and server development Big Data & Cloud Infrastructure Knowledge of building and deploying cloud More ❯

science stack and ecosystem (such as Pandas, NumPy, Jupyter notebooks, SciPy, FastAPI, Flask, Matplotlib, and similar) Core ML and DL frameworks (such as PyTorch (strongly preferred), Keras, TensorFlow, scikit-learn, and similar) Cloud compute, infrastructure, services, and deployment w.r.t. end-to-end data science (ideally AWS (such as S3, EC2, Lambda, ECR, ECS)) Data visualisation methods and tools More ❯

tooling to get bootstrapped quickly is a must. Core AI & Machine Learning Python Vertex AI/Hugging Face LangChain/BAML — LLM frameworks Langfuse, LangSmith — Observability Pandas, NumPy, scikit-learn, PyTorch — Data & ML stack Data & Infrastructure BigQuery — Cloud data warehouse PostgreSQL — Application data Pulumi — Infrastructure as Code (TypeScript) Google Cloud Platform (GCP) — Cloud provider GitHub Actions — CI/ More ❯

Proven expertise in MLOps implementation for deploying, monitoring, and managing ML models in production environments. Proficiency in Python and experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy Strong understanding of cloud technologies and AI/ML platforms, particularly AWS SageMaker. Solid grasp of software engineering principles including design patterns, testing, CI/CD 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 ❯

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 ❯

focused experience in AI and data-driven architecture design. • Extensive experience in enterprise architecture frameworks (e.g., TOGAF, Zachman). • Expertise in AI/ML technologies (e.g., TensorFlow, PyTorch, Scikit-learn, Keras) and understanding of deep learning and natural language processing (NLP). • Strong understanding of big data platforms such as Hadoop, Apache Spark, and data lakes. • Hands-on More ❯

scalable data processing tools. AWS Ecosystem – Leverage services like SageMaker, S3, Glue, and Athena for data engineering and ML model deployment. ML Frameworks – Work with tools such as scikit-learn, TensorFlow, or similar libraries to experiment and optimize models. Version Control – Use Git and CI/CD tools to manage code and streamline development workflows. Data Visualization – Communicate More ❯

monitoring , and adoption of emerging AI tech. What We’re Looking For 5+ years in data engineering, with team or project leadership experience. Advanced Python (Pandas, PyTorch, TensorFlow, Scikit-learn). Strong AWS/GCP , MySQL , and CI/CD experience; Docker/Kubernetes a plus. Excellent communication, organisation, and problem-solving skills. Passion for innovation and ethical More ❯

5+ years of experience as an ML Engineer or Software Engineer with ML focus 5+ years of proficient experience with Python, strong experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with distributed training and optimization on GPUs (CUDA, RAPIDS) Familiarity with data pipelines (Spark, Databricks, Kafka) Hands-on experience with CI/CD for ML workflows and More ❯

predictive modeling, forecasting, and statistical analysis. Hands-on experience with tools such as Python, R, SAS, or similar analytics technologies. Proficiency with machine learning libraries such as TensorFlow, Scikit-learn, or PyTorch. Strong SQL skills and experience working with large relational or cloud data environments. Ability to interpret and communicate complex analytic results to non-technical audiences. Experience More ❯

machine learning, with at least 2 years supporting federal or defense programs. * Must possess proficiency with *Maven, Vantage, TDP and Advana.* * Proficiency in Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch) Experience with data wrangling, feature engineering, and model evaluation in secure or air-gapped environments. * Familiarity with DoD data systems, cybersecurity protocols, and cloud platforms (e.g. More ❯

one AI-focused programming language (Python required; Java preferred Strong experience with cloud platforms such as AWS, Azure, or GCP. Solid understanding of machine learning frameworks (TensorFlow, PyTorch, scikit-learn Experience working with CI/CD platforms (Jenkins, GitHub, GitLab CI, Artifactory Hands-on experience with automated vulnerability detection and remediation using DAST/SAST/IAST scanning More ❯

or technical field (Statistics, Mathematics, Physics, Computer Science, Machine Learning) Strong programming skills in Python (production-level) and SQL; confident with modern ML/AI libraries such as scikit-learn, TensorFlow, or PyTorch Familiarity with MLOps frameworks, model deployment, and cloud-based platforms (Databricks, AWS, Azure) Strong experience with data visualisation tools and techniques; able to turn complex More ❯