Data Scientist - Credit Behaviours

NewDay
London
4 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist (Predictive Modelling) – NHS

Data Scientist - Measurement Specialist

Data Scientist, United Kingdom - BCG X

Data Scientist, United Kingdom - BCG X

Data Scientist

Join to apply for the Data Scientist - Credit Behaviours role at NewDay.

Overview

Data Scientist – Credit Behaviours role focused on applying statistical and machine learning techniques to identify trends and relationships in data, sourcing and prototyping data to create value for NewDay and customers, and governance throughout model lifecycle.

Responsibilities
  • Use sophisticated statistical and machine learning techniques to identify new trends and relationships in data.
  • Harvest, wrangle and prototype new data sources internally and externally 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.
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.
Your Skills and Experience
  • 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 and 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.
Employment details
  • Permanent, Full-time
  • Location: London, England, United Kingdom

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


#J-18808-Ljbffr

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.