responsibility for system architecture, mentoring junior team members, and conducting thorough code reviews Strong programming skills in Python and C++, with experience using libraries and frameworks such as PyTorch, NumPy, Pandas, TensorFlow, and OpenCV for computer vision and data processing Familiarity with front-end technologies including JavaScript and HTML for building user-facing interfaces or tools Practical, hands-on experience More ❯
constraints. Automate Data Pipelines, designing and managing workflows for collecting, cleaning, and storing large volumes of financial data (e.g., price, volume, fundamentals, alternative data), often using tools like Pandas, NumPy, and Dask. Collaborate Across Teams for Deployment, working with researchers, traders, and DevOps teams to integrate Python models into production environments (e.g., through APIs, microservices, or containerized systems like Docker More ❯
Skills and Experience: Proven ability to solve complex, real-world problems through data science and analytics. Experience coaching and reviewing work of junior team members. Strong Python skills (pandas, numpy, scikit-learn) and a solid grounding in probability and statistics. Deep knowledge of machine learning methods and their practical application. Experience managing multiple end-to-end data science projects across More ❯
and delivery of solutions. Optimize performance and reliability across data platforms and cloud environments. Document processes and share knowledge effectively. Essential Skills Python (Highly Proficient): Libraries such as Pandas, NumPy; API development (Flask/Dash); package management (pip, Poetry). DevOps Expertise: CI/CD tools (Jenkins, GitHub Actions), scripting (Bash, Python), Linux environments. Cloud & Containerization: Docker, Kubernetes, and experience More ❯
knowledge of advanced predictive modeling, optimization, scenario analysis, and statistical methodologies. Strong grasp of supervised/unsupervised methods, evaluation metrics, feature engineering, and model tuning. Proficiency in Python (pandas, NumPy, Scikit-learn); experience with PyTorch or TensorFlow for deep learning. Experience with API development and connecting AI systems to external platforms. Working knowledge in deep learning techniques, including CNNs, RNNs More ❯
reusable analytical datasets. Modern BI: hands-on experience with a modern BI platform with integrated semantic layer (e.g Omni, Looker, Thoughtspot) Python for analysis: practical experience with pandas/numpy and notebook-based workflows for LTV, attribution, simulations and experimentation analysis. Domain expertise: deep, practical knowledge in one or more of the following functions & platforms: CRM (e.g. SalesForce), marketing (e.g. More ❯
Technical Experience: Strong software engineering skills with experience in machine learning, numerical computing, and/or applied ML research Proficiency with Python and numerical + ML frameworks (PyTorch, JAX, numpy), familiarity with PyTorch 2.x compiler stack (e.g., TorchScript, TorchInductor) is a plus Practical experience deploying ML models or infrastructure in a research and production environment BS/MS/PhD More ❯
dashboards and visualizations for business teams Communicate findings to non-technical stakeholders What You Bring 5+ years in applied data science or analytics Strong Python or R skills (pandas, NumPy, Jupyter) and solid SQL experience Familiarity with BI tools like Tableau or Power BI Strong foundation in statistics, experimentation, and A/B testing Excellent communication and storytelling abilities Role More ❯
They're Looking For You've led the delivery of applied machine learning projects, ideally across commercial or regulated sectors Strong Python skills and comfort using core libraries (e.g. NumPy, Pandas), plus familiarity with deep learning tooling like PyTorch Expertise in a wide range of ML methods, including supervised and unsupervised learning, time series, or NLP and LLM/GenAI More ❯
