101 to 125 of 261 scikit-learn Jobs

Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks (Desirable): Azure ML, AWS Sagemaker, HuggingFace, OpenAI APIs Version Control More ❯

Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks (Desirable): Azure ML, AWS Sagemaker, HuggingFace, OpenAI APIs Version Control More ❯

Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks (Desirable): Azure ML, AWS Sagemaker, HuggingFace, OpenAI APIs Version Control More ❯

Tech Stack You'll Work With BI Tools : Power BI, Tableau, Looker, Google Data Studio Data Warehousing : Snowflake, BigQuery, Redshift, SQL Server Languages : SQL, DAX, Python (Pandas, NumPy, Scikit-learn - desirable) CRM Systems : Salesforce, HubSpot, Dynamics 365, Zoho ETL Tools : dbt, Fivetran, Talend, Alteryx AI/ML Frameworks (Desirable): Azure ML, AWS Sagemaker, HuggingFace, OpenAI APIs Version Control More ❯

What we're looking for 6+ years of experience in software engineering with strong focus on machine learning systems Deep proficiency in Python and ML ecosystem (e.g. PyTorch, scikit-learn, MLFlow) Solid understanding of data and model lifecycle management, versioning, and deployment Experience building ML infrastructure and model-serving pipelines in production environments Familiarity with cloud-based architecture More ❯

5+ years of experience in machine learning engineering, preferably in Ad Tech, Mar Tech, or related high-scale environments. Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Deep understanding of supervised, unsupervised, and reinforcement learning methods. Experience with GCP services such as Vertex AI, BigQuery ML, Dataflow, AI Platform Pipelines, and Dataproc. Solid knowledge More ❯

5+ years of experience in machine learning engineering, preferably in Ad Tech, Mar Tech, or related high-scale environments. Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Deep understanding of supervised, unsupervised, and reinforcement learning methods. Experience with GCP services such as Vertex AI, BigQuery ML, Dataflow, AI Platform Pipelines, and Dataproc. Solid knowledge More ❯

5+ years of experience in machine learning engineering, preferably in Ad Tech, Mar Tech, or related high-scale environments. Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Deep understanding of supervised, unsupervised, and reinforcement learning methods. Experience with GCP services such as Vertex AI, BigQuery ML, Dataflow, AI Platform Pipelines, and Dataproc. Solid knowledge More ❯

deep learning methods and machine learning - Experience in building machine learning models for business application - Experience in applied research 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 Amazon is an equal opportunities employer. We believe passionately More ❯

and optimisation techniques-including supervised/unsupervised learning and operations research (e.g. linear, mixed-integer programming, heuristics). Proficient in Python (required), with experience using libraries such as scikit-learn, pandas, numpy, and Gurobi. Other programming languages are a plus. Solid experience with SQL, data engineering, and cloud-based tools (AWS preferred), as well as version control (Git More ❯

of key machine learning models, including Gradient Boosting Machines (GBMs), Neural Networks and Large language models (LLMs). Hands-on experience with popular machine learning libraries such as Scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch. Knowledge of AWS products and services including Sagemaker. Deep knowledge of Microsoft Excel in a commercial setting. You enjoy being Agile - you should More ❯

AI services, model deployment, monitoring, and CI/CD pipelines for ML models (MLOps best practices). Example Tools & Technologies: Frameworks & Libraries: LangChain, Hugging Face Transformers, PyTorch, TensorFlow, Scikit-learn Agentic AI Tools: OpenAI GPT models, Crew,AI, Cohere, Pinecone (for vector databases), AutoGPT Data Engineering & ML Pipelines: Apache Airflow, MLflow, Kubeflow, dbt, Prefect Cloud & Deployment Platforms: AWS More ❯

from pipelining to model deployment , Experience with some of the following tools and technologies (or an eagerness to learn): , Python and its data science ecosystem (e.g., pandas, scikit-learn, TensorFlow/PyTorch) , Statistical methods and machine learning (e.g., A/B testing, model validation) , Data pipelining tools like SQL, dbt, BigQuery, or Spark , A strong communicator with More ❯

us again, and again. Proven track record delivering impactful ML/AI solutions in production. Deep expertise in Python and modern AI/ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, NumPy, Pandas). Hands-on experience with GenAI, agentic AI, and automated testing for AI systems. Curiosity and creativity to challenge assumptions and explore new approaches. Strong communication More ❯

example: supervised/unsupervised machine learning, model cross-validation, Bayesian inference, and time-series analysis An excellent proficiency of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch) Demonstrable experience enabling impactful change with AI within a financial services institution. Coupling that with prior experience in More ❯

prem equivalents (e.g., logging, tracing, metrics). Knowledge of data processing frameworks (e.g., Pandas, Spark, Airflow) is a plus. Comfortable reading and working with Python-based ML code (scikit-learn, TensorFlow, PyTorch, etc.). Strong ownership mindset and a collaborative attitude. Nice to Have Experience with model versioning and ML serving frameworks (e.g., MLflow, Seldon, Triton). Understanding More ❯

PhD (or equivalent experience) in a relevant discipline Deep knowledge of machine learning and statistical signal processing Strong Python skills and familiarity with frameworks like PyTorch, TensorFlow, and scikit-learn Experience working with time-series, sensor, or RF data Track record of delivering innovative solutions in complex technical domains Please note: Due to the nature of the work More ❯

Python) CI/CD pipelines, unit testing, use of version control systems. Dashboarding and Data Visualization Skills (e.g. Streamlit, Dash, Retool, Plotly) Exposure to ML libraries/systems (Scikit-learn, PyTorch, MLflow) Clinical trials Good Clinical Practice (GCP) Medical device development About Us All our benefits information can be found in the downloadable Benefits document under 'Information' on More ❯

data science or a quantitative academic field. Strong programming skills, with the ability to quickly become fluent in Python. Deep knowledge of core data science libraries (NumPy, Pandas, Scikit-Learn) and at least one deep learning framework (TensorFlow, PyTorch, or similar). High mathematical and statistical competence, with the ability to design new algorithms when needed. Experience leading More ❯

and multi-objective optimization using machine learning and/or deep learning methods Experienced with common python toolkits for scientific computing (e.g., numpy, pandas, scipy), machine learning (e.g., scikit-learn, pytorch), and cheminformatics/bioinformatics (e.g., rdkit, openeye, biotite, biopython) Familiarity running simulations and training models on high-performance computing (GPU) environments for corporate R&D, innovation labs More ❯

from pipelining to model deployment. Experience with some of the following tools and technologies (or an eagerness to learn): Python and its data science ecosystem (e.g., pandas, scikit-learn, TensorFlow/PyTorch). Statistical methods and machine learning (e.g., A/B testing, model validation). Data pipelining tools like SQL, dbt, BigQuery, or Spark. A strong More ❯

Docker). Strong Python programming skills: Strong programming skills in Python, including its ecosystem for AI/ML, with experience using libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and TensorFlow to build and implement AI-driven solutions. Product mindset: Ability to think from the user's perspective and design solutions that align with developer needs, improving More ❯

Docker). Strong Python programming skills: Strong programming skills in Python, including its ecosystem for AI/ML, with experience using libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and TensorFlow to build and implement AI-driven solutions. Product mindset: Ability to think from the user's perspective and design solutions that align with developer needs, improving More ❯

Docker). Strong Python programming skills: Strong programming skills in Python, including its ecosystem for AI/ML, with experience using libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and TensorFlow to build and implement AI-driven solutions. Product mindset: Ability to think from the user's perspective and design solutions that align with developer needs, improving More ❯

Docker). Strong Python programming skills: Strong programming skills in Python, including its ecosystem for AI/ML, with experience using libraries such as PyTorch, Hugging Face Transformers, scikit-learn, and TensorFlow to build and implement AI-driven solutions. Product mindset: Ability to think from the user's perspective and design solutions that align with developer needs, improving More ❯