Associate Director, Data Analytics - Value Creation & Deals

Interpath
Glasgow
2 months ago
Applications closed

Related Jobs

View all jobs

Associate Director - Data Analytics

Data Analyst - Associate Director - Belfast

Associate Data Analyst

Systems and Data Analyst (Milton Keynes, ENG, GB, MK7 6AA)

Data Engineer

Trainee Data Analyst Associate

Associate Director, Data Analytics – Value Creation & Deals

Interpath is an international, fast‑growing advisory business with deep expertise in deals, advisory and restructuring. With a presence across the UK, Ireland, France, Germany, Austria, Spain, BVI, Cayman Islands, Bermuda, Barbados and Hong Kong, Interpath delivers tangible results for global businesses when complex problems arise.


The role is based in a key UK city (Leeds, Birmingham, Belfast, Manchester or Glasgow) and is part of Interpath’s Data & Technology consulting group. The successful candidate will help develop the Data Analytics team and bring fresh data solutions to a wide range of client projects, from customer profiling to supply‑chain optimisation.


Key Accountabilities

  • Deals Analytics support across transaction and advisory service lines
  • Build new data capabilities covering customer profiling, segmentation, profitability and demand forecasting
  • Create a Data Insight playbook and blue‑print architecture for value creation projects
  • Accelerate delivery timelines through better solutions and new ways of thinking
  • Act as central point of contact for the team, fostering strong working relationships
  • Lead change management and show the “art of the possible” in client engagements
  • Extract information and provide insightful reports using advanced techniques
  • Design meaningful dashboards to inform strategy, predict trends and deliver data story‑telling to clients
  • Work with the data team to determine the best data infrastructure to maximise analysis

Requirements

  • At least 4 years of experience in data analytics within a consulting context
  • University degree 2.1 or higher in Computer Science, Mathematics, Statistics or equivalent
  • End‑to‑end knowledge of data warehouse and reporting processes
  • Hands‑on, technically strong on data engineering, reporting and analysis
  • Strong consulting skills and the ability to apply business intelligence and data analytics techniques in a commercial setting
  • Commercially savvy, articulate in data storytelling and a trusted adviser to key stakeholders
  • Proficient in PowerBI, Qlik, Tableau, Microsoft SQL, Python/R
  • Strong knowledge of statistical methodologies and clustering techniques
  • Proven experience of cloud technologies (AWS, Azure, GCP)
  • Numerate and analytical with knowledge of data management
  • Passionate about data analytics, technically self‑sufficient and keen to research new tools and techniques quickly
  • Ability to visualise data effectively and communicate findings and recommendations clearly

Advantageous Competencies (but not essential)

  • Exposure to AI/ML and the open‑source stack
  • Experience in price modelling techniques
  • Experience managing a small data team and mentoring
  • Some exposure to behavioural data (e.g., Google or Adobe analytics)

Benefits

At Interpath, employees receive a competitive and comprehensive reward package, including compelling salaries and a range of core and optional benefits. Read more about our benefits on the Interpath company benefits page.



#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.