151 to 175 of 205 NumPy Jobs

compliance to drive data-informed decisions What we're looking for: 4-8 years experience in data science, ideally in fintech, payments, or crypto Strong SQL + Python (Pandas, NumPy, scikit-learn) and experience with BI tools Ability to analyze complex datasets - from internal ledgers to on-chain activity Stablecoin or crypto analytics experience is a big plus Strong communicator More ❯

scientists to develop and productionise models Troubleshoot, debug, and refine model code Partner with cross-functional teams to translate business needs into data-driven solutions Essential skills: Python (pandas, NumPy, scikit-learn) – data wrangling, modelling, feature engineering SQL – querying structured datasets Model Development & Validation – classification, unsupervised learning (outlier detection), ranking models ML Deployment – containerised deployments (Podman, SageMaker, DSW pipelines) Git More ❯

scientists to develop and productionise models Troubleshoot, debug, and refine model code Partner with cross-functional teams to translate business needs into data-driven solutions Essential skills: Python (pandas, NumPy, scikit-learn) – data wrangling, modelling, feature engineering SQL – querying structured datasets Model Development & Validation – classification, unsupervised learning (outlier detection), ranking models ML Deployment – containerised deployments (Podman, SageMaker, DSW pipelines) Git More ❯

ICLR, CVPR, ECCV, IEEE TPAMI, AAAI). Experience in Linux environments. Proficiency in programming languages such as Python, Java, or C++. Experience with machine learning libraries like PyTorch, SciKit, NumPy, etc. Excellent communication skills, teamwork abilities, and a results-oriented attitude. Strong problem-solving and debugging skills. Desirable Skills: Expertise in Image/Video generation, PEFT-LoRA, Diffusion/Autoregressive More ❯

sport, gaming, or similar domains with forecasting needs. 2+ years working with Python or R in a production or research setting. Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. More ❯

sport, gaming, or similar domains with forecasting needs. 2+ years working with Python or R in a production or research setting. Strong experience using data wrangling tools (e.g. Pandas, NumPy, dplyr). Solid grasp of statistical modelling and machine learning, with hands-on use of libraries such as scikit-learn, xgboost, PyMC3, TensorFlow. Experience working with SQL and relational databases. More ❯

e.g., ICML, NeurIPS, ICLR, EMNLP, CVPR, ECCV, IEEE TPAMI, AAAI). Proficient in programming languages like Python, Java, or C++, and experienced with Machine Learning libraries (e.g., PyTorch, SciKit, NumPy). Comfortable working in Linux environments and collaborating in a hybrid team setup. Exceptional communication, teamwork, and problem-solving skills. Why Join Us? Be part of a team that’s More ❯

queries, optimise performance, and manipulate large datasets across relational databases. • Strong Python Proficiency: Skilled in using Python for data analysis, and automation with experience in libraries such as Pandas, NumPy, and SQLAlchemy. • Data Modelling: Handle and model large amounts of data, create solutions to solve problems and to answer key business questions • Data Structures: Strong Understanding of Data Structures working More ❯

learning models to production Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, or Engineering - or equivalent Proficiency in Python and relevant data science libraries (NumPy, pandas, scikit-learn etc.) Experience with SQL, Power BI, Git & GitHub Strong knowledge of Machine Learning Algorithms and respective theory Ability to work within a team, collaborating effectively with colleagues More ❯

techniques and SoC-based modular designs Understanding of firmware development processes, from simulation and testbench creation through to embedded implementation Familiarity with tools/libraries such as MATLAB, Python (NumPy/SciPy), GNU Radio or TensorFlow for DSP/ML integration Comfortable working in Linux and/or Windows development environments Why this role? Be part of a company trusted More ❯

experience in a 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 More ❯

experience in a 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 More ❯

experience in a 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 More ❯

experience in a 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 More ❯

Scientist, your background will include: 3+ years industry experience in a Data Science role and a strong academic background Python Data Science Stack: Advanced proficiency in Python , including pandas , NumPy , scikit-learn , and Jupyter Notebooks . Statistical & ML Modelling: Strong foundation in statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression More ❯

Science with reasonable industry experience, or an MS with significant industry or research experience in the field • Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas) • Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals • Experience with big data More ❯

pushing the boundaries You may be a good fit if you have: Strong software engineering skills Proficiency in Python and have some experience with ML-related code (e.g., pytorch, numpy, etc.) Experience with LLMs and agentic frameworks Experience with post-training LLMs (SFT, PEFT, or RL ) Experience with building synthetic data generation pipelines If some of the above doesn't More ❯

or transferable from other languages (e.g. R, MATLAB, C). Solid mathematical reasoning and knowledge of statistics and probability. Experience with machine learning algorithms and data manipulation libraries (e.g. NumPy, Pandas, Scikit-Learn). Strong communication skills and ability to present work confidently to stakeholders. Ability to follow a plan, meet deadlines, and adapt when challenges arise. Bonus skills: Prior More ❯

in a data science or analytics role, preferably within the AI/ML industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks More ❯

in a data science or analytics role, preferably within the AI/ML industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks More ❯

in a data science or analytics role, preferably within the AI/ML industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks More ❯

in a data science or analytics role, preferably within the AI/ML industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks More ❯

in a data science or analytics role, preferably within the AI/ML industry. Expert-level proficiency in Python and extensive experience with data science libraries such as Pandas, NumPy, and Scikit-learn. Deep hands-on experience with deep learning frameworks, particularly PyTorch . Proven experience with statistical analysis , hypothesis testing, and data visualization techniques. Familiarity with computer vision tasks More ❯

with a focus on areas such as robustness, explainability, or uncertainty estimation. Advanced programming and mathematical skills with Python and an experience with the standard Python data science stack (NumPy, pandas, Scikit-learn etc.) The ability to conduct and oversee complex technical research projects. A passion for leading and developing technical teams; adopting a caring attitude towards the personal and More ❯

statistical techniques, for 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 More ❯