Contribute to the continuous improvement of development processes, including CI/CD and automated deployment pipelines. Required Skills & Experience Strong proficiency in Python, with experience in libraries such as Pandas, NumPy, and application frameworks (e.g., Flask, FastAPI, or similar). Solid understanding of software engineering principles, including object-oriented design and modular architecture. Experience building applications for front office environments More ❯
ML models into production. Prior experience leading projects or teams is a plus for a lead role Programming & ML Skills: Advanced programming skills in Python (including libraries such as pandas, scikit-learn, TensorFlow/PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps More ❯
data cycle. - Proven Experience working with AWS data technologies (S3, Redshift, Glue, Lambda, Lake formation, Cloud Formation), GitHub, CI/CD - Coding experience in Apache Spark, Iceberg or Python (Pandas) - Experience in change and release management. - Experience in Database Warehouse design and data modelling - Experience managing Data Migration projects. - Cloud data platform development and deployment. - Experience of performance tuning in More ❯
maintaining NLP/Deep Learning Systems. * Extensive experience in at least one Deep Learning framework (PyTorch, TensorFlow, JAX). * Well versed in the scientific Python ecosystem (NumPy, Scikit-Learn, Pandas etc.). * Strong Data Engineering underpinnings and an ability to work with big data (Tbs). * Strong leadership skills, using empathy, clarity and challenge to support your team to be More ❯
and building 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 More ❯
Develop 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 More ❯
design, rubric structure, and evaluation criteria. Communicate insights to data labeling experts and technical teams. Requirements Strong foundation in statistical analysis, hypothesis testing, and pattern recognition. Proficiency in Python (pandas, scipy, matplotlib/seaborn) or R for data analysis. Experience with exploratory data analysis and deriving actionable insights from complex datasets. Familiarity with LLM evaluation methods and quality metrics. Comfortable More ❯
