Data Scientist

Randstad; United Kingdom; Hybrid; GBP 50,000 - 70,000; Full time

Job summary

A global staffing firm is seeking a skilled Data Scientist for a 6-month project in AI within Commercial Banking. This hybrid role involves leading data science activities from data collection to insight generation, collaborating closely with client teams. Applicants should have strong analytics skills and experience with visualization tools like Power BI and Python. Knowledge of machine learning techniques is essential.

Qualifications

• Proven ability to translate model outputs into actionable insights.
• Experience in building reports and dashboards.
• Strong communication skills for engaging stakeholders.
• Experience with business use cases and metrics definition.
• Familiarity with Agile environments.

Responsibilities

• Collect and clean structured and unstructured data.
• Perform exploratory data analysis to uncover insights.
• Design and build data pipelines with engineering teams.
• Develop and validate machine learning models.
• Collaborate with teams to transition models into production.

Skills

Data storytelling

Dashboard building

Strong communication skills

Agile methodologies

Tools

Power BI

Tableau

Python

Scikit-learn

TensorFlow

PyTorch

Job description

Senior Delivery Consultant | Recruitment Advisor | Internal Recruitment | MSP | RPO

📍 Location: Hybrid – mainly remote. Occasional client travel may be required (pre‑approved and reimbursed by the client).

• (Inside IR35)

About the Role

We’re seeking a skilled Data Scientist to join our Data & Analytics team for a 6‑month project focused on AI in Commercial Banking.

You’ll lead end‑to‑end data science activities from data collection and cleaning to analysis, modelling, and insight generation working closely with client teams to deliver actionable, AI‑driven outcomes that power smarter business decisions.

Responsibilities

• Collect, clean, and preprocess structured and unstructured data from diverse internal and external sources.
• Perform exploratory data analysis (EDA) to uncover patterns, trends, and anomalies.
• Design and build data pipelines with engineering teams to produce model‑ready datasets.
• Apply feature engineering and selection techniques to enhance model accuracy and interpretability.
• Develop and validate machine learning and statistical models for predictive, classification, clustering, or optimization tasks.
• Implement supervised and unsupervised learning algorithms using Scikit‑learn, TensorFlow, or PyTorch.
• Apply advanced techniques such as NLP, time‑series forecasting, and optimization algorithms when required.
• Evaluate and fine‑tune models with appropriate metrics and hyperparameter optimization.
• Collaborate with MLOps and engineering teams to transition proof‑of‑concept models into production‑grade solutions.

Required Skills & Experience

• Proven ability to translate model outputs into clear, actionable business insights through compelling data storytelling and visualization.
• Experience building dashboards and reports with Power BI, Tableau, or Python‑based visualization tools.
• Strong communication skills to engage both technical and non‑technical stakeholders.
• Experience working with business analysts, architects, and domain experts to define use cases and success metrics.
• Contribution to enterprise AI roadmaps and a passion for promoting best practices in analytics and modelling.
• Thorough documentation of methodologies, model logic, and validation results for audit and reproducibility.
• Familiarity with Agile environments, participating in sprint planning, stand‑ups, and client showcases.

Employment

• Seniority level: Mid‑Senior level
• Contract
• Industry: Information Technology