How Remoteville checks and expires listings
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Masters degree in Data Science, Machine Learning, Artificial Intelligence, or related quantitative discipline
- Circa 5+ years of commercial experience working on complex problems, varied data types, and machine learning (ML)/artificial intelligence (AI) approaches with multiple live solution deployments
- Excellent familiarity with the data science process: experiment/hypothesis formation, data selection, feature engineering, model selection, sampling/cross-validation, hyperparameter tuning, model performance metrics, code, and notebook documentation
- Experience of working in a Cloud environment (AWS preferred)
- Experience across common supervised, unsupervised, semi-supervised, reinforcement machine learning techniques
- Accomplished Python coder
- Data wrangling and data engineering using Python and SQL
- Ability to use dimension reduction techniques (PCA, encoders)
- Familiarity with elastic net logistic regression, random forest, and XGBoost ensembles for supervised problems with structured data
- Classification using K-means, K-Medoids, or similar, with skills to evaluate solution suitability
- Skills to deploy deep learning, ideally through TensorFlow, including transformer architectures
- NLP capabilities, including sentiment analysis and localizing large language models, ideally through Hugging Face
- Strong math skills for fundamental understanding and novel applications
- Forecasting using time-series and related approaches, using classical and modern techniques
Nice to have
- deploying graph analytics
- deep feature synthesis frameworks
- transactional data
Day to day
- Researching, building, deploying, and monitoring machine learning (ML) models, in close collaboration with cross-functional teams.
- Supporting clients through enhanced decision-making and automation, utilizing a developing cloud-based AWS data platform.
- Liaising with clients through events, conferences, webinars etc., to assist their data-driven journeys.