Data Scientist

mthree; Sheffield; On-site; GBP 60,000 - 80,000; Full time

Job summary

mthree is seeking a Data Scientist to join our Global Technology Division, focused on predictive enterprise optimization and internal mobility platforms. You will design and deploy recommendation models and matching indexes balancing business constraints with operational overhead.

You will collaborate with upstream data teams to track model metrics, monitor drift, and surface confidence scores to executives.

Qualifications

• Intermediate experience as a Data Scientist, Machine Learning Engineer, or Quantitative Analyst in an enterprise environment (Fintech, Banking, or Scale-up SaaS preferred).
• Fluency in Python and ML libraries (scikit-learn, SciPy, statsmodels) with NLP exposure (spaCy, HuggingFace) valued.

Responsibilities

• Develop, tune, and maintain semantic matching algorithms and recommendation models.
• Build predictive optimization models for personnel distribution and cost parameters.
• Ensure statistical rigor and fairness in models and evaluate algorithmic performance.
• Collaborate with upstream data teams to track model metrics and surface confidence scores.

Skills

Python

NLP frameworks

Statistical modeling

Data visualization

Exploratory data storytelling

Tools

scikit-learn

SciPy

statsmodels

spaCy

HuggingFace

Plotly

Seaborn

Job description

We are seeking a Data Scientist to join our Global Technology Division. This role sits within a highly strategic engineering team focused on predictive enterprise optimization and internal mobility platforms. You will hold the keys to a core mathematical engine, designing and deploying sophisticated recommendation models and matching indexes that mathematically balance complex business constraints against corporate operational overhead.

Key Responsibilities

• 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.

Required Technical Skills

• 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.