1 to 25 of 288 Permanent scikit-learn Jobs

environment (AWS, Snowflake, Azure) with large datasets and with streaming data architectures. Demonstrated knowledge of large language models and generative AI. ML frameworks (Scikit-Learn, NumPy, SciPy, Pandas, XGBoost, Tensorflow, PyTorch, MXNet, LLM) Qualifications Required: Bachelor's Degree with 7 years experience; Master's Degree with 6 years More ❯

Technical Skills: Programming & Data Tools Proficiency in Python and R for machine learning and data analysis. Experience with libraries like Pandas, NumPy, and scikit-learn for data manipulation and model building. Familiarity with data visualization tools such as Matplotlib or Plotly. Machine Learning & Model Development Experience developing and More ❯

Advanced Python programming skills, with a strong emphasis on writing efficient, scalable, and maintainable code. Proven experience with TensorFlow/PyTorch/Jax, Scikit-learn, and MLOps workflows for training, deployment, and monitoring of ML models. Experience working with Polars and/or Pandas for high-performance data More ❯

s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field. Experience with Python, SQL, and machine learning frameworks (e.g., TensorFlow, Scikit-learn, PyTorch). Familiarity with software development, cloud computing (AWS, Azure), and algorithms. Strong analytical and problem-solving skills with the ability to work More ❯

master's with 6 years, or PhD with 4 years Proficiency in data science languages and tools (e.g., Python, R, SQL, Jupyter, Pandas, Scikit-learn) Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and big data platforms (e.g., Spark, Hadoop) Strong background in statistics, data modeling, and algorithm More ❯

JupyterLab or AWS Sagemaker for interactive data analysis and model development. Strong proficiency in Python and experience with libraries such as TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy and Statsmodels. Expertise in time series forecasting techniques, such as RNNs, LSTMs and GRUs. Knowledge of Grafana (or similar tools) and More ❯

techniques, with hands-on experience in either academia or industry. Proficiency in Python and experience with ML/NLP libraries like TensorFlow, PyTorch, scikit-learn, or Hugging Face. Experience working with cloud platforms such as AWS, GCP, or Azure. Familiarity with advanced NLP methods, including Prompt Engineering, Parameter More ❯

or similar scientific computing languages. Strong background in biologics, bioinformatics, project management, and DevOps Proficiency in data science analysis libraries (e.g., pandas, numpy, scikit-learn) Experience with machine learning frameworks (e.g., TensorFlow, PyTorch). Knowledge of cloud platforms (e.g., AWS, Azure, Google Cloud ) and HPC environments Ability to More ❯

software engineering skills, including version control (Git), code reviews, and unit testing. Familiarity with common data science libraries and tools (e.g., NumPy, Pandas, Scikit-learn, Jupyter). Experience in setting up and managing continuous integration and continuous deployment pipelines. Proficiency with containerization technologies (e.g., Docker, Kubernetes). Experience More ❯

in NLP or classification problems. Hands on experience with ML tools like TensorFlow, PyTorch etc. Experience with data science libraries such as NLTK, Scikit-learn, SciPy, (Sci)SpaCy etc. Excellent problem-solving and programming skills in Python Excellent communication skills Preferred Qualifications & Skills: If you have the following More ❯

Other languages include: Shell, PERL, Java, C/C++/C#, Scala, etc. -Exceptional technical fluency in machine learning frameworks (such as TensorFlow, Scikit-Learn, etc.) Personal Qualities: -Ability to contextualize data as it relates to Rhombus' vision. We want someone who can understand the "why" and "so More ❯

Bachelor's, Master's, or PhD in Computer Science, AI, Data Science, or a related field. Programming Skills: Proficiency in Python, TensorFlow, PyTorch, Scikit-learn, or similar frameworks. Machine Learning & AI Expertise: Strong knowledge of supervised, unsupervised, and reinforcement learning. Data Handling: Experience with big data technologies (Hadoop More ❯

to fine-tune those models e.g. XGBoost, Deep Neural Networks, Transformers, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, Pytorch, scikit-learn, etc. Must demonstrate the capacity of reading, understanding and implementing new techniques in the field of machine learning as they emerge. Experience of More ❯

general programming languages: Python, C++, Java, etc. Experience with deep learning, machine learning and NLP frameworks such as PyTorch (or TensorFlow), HuggingFace Transformer, Scikit-learn Experience with working in Linux A strong intuition for what makes products a joy to use Empathy for how different users will need More ❯

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 More ❯

/ML models. Proficiency in programming languages such as Python, with experience in relevant AI/ML libraries and frameworks (eg, TensorFlow, PyTorch, scikit-learn). Excellent communication, interpersonal, and presentation skills. Strong analytical and problem-solving skills. Ability to work independently and as part of a team. More ❯

production-ready code, architect packages, and make sound architectural and procedural choices for effective shared codebases • Python and scientific computing packages (pytorch, numpy, scikit-learn, tensorflow) • Database technologies such as ElasticSearch, Neo4j, and SQL • Excellent interpersonal, verbal, and written communication skills • A flexible attitude with respect to work More ❯

like Flask, FastAPI, Pandas, PySpark, PyTorch, to name a few. Proficiency in statistics and/or machine learning libraries like NumPy, matplotlib, seaborn, scikit-learn, etc. Experience in building ETL/ELT processes and data pipelines with platforms like Airflow, Dagster, or Luigi. What's important for us 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 ❯

modeling techniques, and data mining principles. Proficiency in programming languages such as Python or R, and experience with data science libraries (e.g., Pandas, Scikit-learn, TensorFlow). Experience with data visualization tools such as Tableau or Power BI. Familiarity with Agile methodologies and MBSE principles is a plus. More ❯

security principles, including threat detection, incident response, and security operations. Technical Expertise: Proficiency in Python, SQL, and relevant data science libraries (e.g., pandas, scikit-learn, TensorFlow) and experience working with SIEM tools like Splunk and Elastic Search. Advanced Analytics & ML: Proven experience in applying machine learning techniques (anomaly More ❯

Maths, Stats, Computer Science, Engineering, etc.) from a top university. Strong foundation in statistics, probability, and applied mathematics. Proficiency in Python (Pandas, NumPy, Scikit-learn). Experience with cloud platforms (AWS, Azure, or GCP) for data processing or model deployment. Familiarity with SQL and relational databases. Exposure to More ❯

and deploying AI solutions in a business environment. Proficiency in Python and familiarity with AI frameworks and libraries such as Pandas, NumPy, PyTorch, Scikit-Learn, Streamlit, and SciPy. Strong understanding of software development principles, AI engineering, and MLOps. Experience working with cloud-based AI solutions on platforms like More ❯

and deploying AI solutions in a business environment. Proficiency in Python and familiarity with AI frameworks and libraries such as Pandas, NumPy, PyTorch, Scikit-Learn, Streamlit, and SciPy. Strong understanding of software development principles, AI engineering, and MLOps. Experience working with cloud-based AI solutions on platforms like More ❯

modeling, and/or NLP. Proficiency in programming languages such as Python, R, or Julia, with experience using ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Familiarity with biological datasets such as genomic, transcriptomic, proteomic, or imaging data. Experience working with large-scale datasets and cloud computing platforms More ❯