Data Scientist

PHMG
Manchester
2 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist Placement

Overview

Role: Data Scientist


Location: Manchester – Hybrid working


Hours: Monday-Friday – 8:45am – 5:15pm


Package: £70k OTE


PHMG is embarking on an exciting new phase in its journey, focusing on modernizing our data capability and enhancing the way we work. This evolution presents numerous opportunities to leverage data more effectively, allowing us to better serve our customers and colleagues. As part of this growth, we are expanding our team to maximize the value from the wealth of data stored within the organisation. To support this vision, we are seeking a Data Scientist to join the Insights function. The Data Scientist will play a crucial role in identifying trends, uncovering patterns, and extracting valuable insights from business data. The ideal candidate will have a proven track record of extracting insights from complex datasets. Sound business understanding and problem-solving abilities will also be required. This role is perfect for individuals passionate about applying analytical methods to foster data-driven decision-making. You will harness your analytical prowess to identify new opportunities for enhancing our business capabilities. We invite you to be part of this exciting journey, marking a new chapter of data-driven excellence at PHMG.


Responsibilities


  • Perform statistical analysis to uncover new commercial opportunities.
  • Develop predictive models to forecast subscription renewals, churn rates, and revenue trends, etc.
  • Profile our client base and prospective clients.
  • Identify opportunities for upselling, cross-selling, and personalised offers based on customer data.
  • Segment the client base into meaningful audiences/profiles.
  • Support ad-hoc analysis requests from the business, providing rapid insights for decision-making.
  • Collaborate closely with the Data Engineering team to improve data models.
  • Present insights and findings to stakeholders in clear, actionable ways.


Requirements


  • Proven experience as a Data Scientist, ideally within a sales or commercial environment.
  • Strong knowledge of data analysis techniques, statistical methods, and machine learning.
  • Proficiency in Python, specifically scikit-learn and pandas.
  • Experience in using MS Fabric desirable.
  • SQL skills would be beneficial.
  • Strong analytical and problem-solving abilities.
  • A can-do attitude and is a self-starter.
  • Comfortable working collaboratively within a cross-functional team.
  • Excellent communication skills, with the ability to present complex insights in a simple, clear way.
  • Proven track record of using insights to drive business change.


Department and context

The Technology Department serves as the central hub for data engineering and insights across our organization. Our department operates as a unified force, cultivating a culture that empowers analysts and engineers to excel. PHMG’s CTO recognizes that effective business intelligence is not the sole responsibility of one team but a capability that should permeate the entire organization. To achieve this, we view the data functions as an enabler of self-serve analytics, accomplished through robust data management, governance, and education initiatives. This approach ensures that business intelligence is accessible and leveraged across the organization, driving data-driven decision-making at all levels.


Why PHMG Tech?

The IT, Data & Engineering functions within PHMG has been going through a digital transformation, changing how we deliver value to our business. A big part of this change is fostering a culture of being open and honest, collaborating, having fun and enabling psychological safety. We want to create a place for teams to do their best work, and you will have the opportunity to influence our decisions, help define standards across the teams and contribute to a healthy and happy working environment.


About PHMG

Established in 1998, PHMG has grown from a renowned Manchester-based business to the world’s leading audio branding agency – working with 36,000 clients in 54 countries across the globe. This expansive client list includes household names of the calibre of Samsung, Audi and Adidas, as well as SMEs in every sector of the global market. We give each of them a stellar production that combines creative copy, world-class voice artistry and an exclusive Brand-Sound-Track – strengthening their business identity in the most memorable, emotive way.


Job details


  • Seniority level: Mid-Senior level
  • Employment type: Full-time
  • Job function: Engineering and Information Technology
  • Industries: Advertising Services


#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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.