Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Scientist - Credit Behaviours

New Day
City of London
5 days ago
Create job alert

What will you be doing day-to-day?

  • Use sophisticated statistical and machine learning techniques to identify new trends and relationships in data.
  • Harvest, wrangle and prototype new data sources internally and external to NewDay to create new value for NewDay and our customers.
  • Provide quality and detailed data science outputs, sharing and following up with as much detail as appropriate or requested by senior managers.
  • Develop knowledge of all relevant data resources within NewDay and in the wider Credit Industry.
  • Governance: support the models throughout their lifecycle from conception, development, implementation, testing and monitoring, with the required level of documentation to follow internal procedures and standards.

Your Skills and Experience

ESSENTIAL

  • At least a BSc or higher university degree in a data science related field (e.g. machine learning, statistics, mathematics)
  • Proficiency in statistical data modelling techniques.
  • Proficiency with Python, including experience with statistics/machine learning packages such as scikit-learn, pandas, numpy, etc.
  • Good SQL/data manipulation skills required including cleaning and managing data.
  • Experience in data visualisation and communication.
  • Experience with working with raw datasets and perform data wrangling pre-modelling.
  • Analytical and problem-solving skills.

DESIRABLE

  • MSc or PhD in Data Science related field (e.g. Machine Learning, Statistics, Mathematics)
  • Experience within a regulated financial services organization.
  • Ability to present sophisticated findings clearly, adapting the level of detail to the audience.
  • Experience in supporting model deployment and working with DevOps/Implementation teams.

Your Personal Attributes

  • Self-motivated, comfortable working in a fast-paced environment where priorities evolve.
  • Honest and hardworking with a will to learn as well as develop others.
  • Strong sense of accountability and ownership, with great organizational, planning and time management skills.
  • Passionate about modelling and techniques to drive value from data.
  • Personable with excellent interpersonal & written communication skills.
  • Ability to build strong and effective working relationships with people across all levels of the organisation.
  • Ability to embrace company culture and embed into day-to-day interactions.
  • Great team spirit, supporting team and colleagues on tasks big and small.

We work with Textio to make our job design and hiring inclusive. Permanent


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist - Palantir

Data Scientist - Remote

Data Scientist Python Software - London (IT) / Freelance

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.