Data Scientist
Mthree
16 days ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
IntermediateJob location
Remote
Tech stack
Software as a Service
Data Presentation
Python
Machine Learning
Natural Language Processing
Recommender Systems
SciPy
Software Deployment
Scikit Learn
Statistics Packages
HuggingFace
Plotly
Spacy
Job description
- Algorithm & Index Design: Develop, tune, and maintain semantic matching algorithms, recommendation engines, or Natural Language Processing (NLP) models to map unstructured text profiles against highly technical corporate frameworks.
- Predictive Optimisation Modeling: Build mathematical optimization models evaluating personnel distribution variables alongside geographic constraints and operational cost parameters to calculate cost-effective resource strategies.
- Upholding Statistical Truth: Champion mathematical and statistical rigor. Ensure all machine learning models accurately handle data imbalances, control for historical performance biases, and rigorously evaluate algorithmic fairness.
- Collaborative AI Deployment: Work closely with upstream data teams to track model metrics, monitor algorithmic prediction drift, and safely surface confidence scores to executive decision-makers.
Requirements
- Experience: Intermediate experience as a Data Scientist, Machine Learning Engineer, or Quantitative Analyst within an enterprise environment (Fintech, Banking, or Scale-up SaaS preferred).
- Python Mastery: Complete fluency in Python and specialized machine learning/statistical libraries (scikit-learn, SciPy, statsmodels). Hands-on exposure to NLP frameworks or text embeddings (spaCy, HuggingFace) is highly valued.
- Statistical Rigor: A solid foundation in applied statistics, including clustering, regression architectures, and predictive modeling validation techniques.
- Exploratory Data Storytelling: Ability to visually explain algorithm performance trends (using Plotly, Seaborn, etc.) and present model logic transparently to senior management.