Senior Data Scientist (Consulting Team)

Project Blackbook LTD
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist and Machine Learning Researcher

Senior Data Scientist

Senior Data Scientist

Role:Senior Data Scientist - Freelance and Permanent Option

Consulting Seniority:Senior Consultant - Director

Day rate:£500-£650/day

Salary (if permanent):£70k-£80k + up to 25%

Project duration:2-3 months or Full time

Location:London/Hybrid

Project Blackbook -We build and manage consultancies' on-demand freelance associate bench through a simple, cost-effective service.

We are partnered with a small but mighty strategy and data consultancy that is continuing to strengthen its data and analytics service lines. They are interested in candidates either on a freelance or permanent basis. What’s key are strong skills in data storytelling and stakeholder engagement.

Please apply directly via our website whenever possible and include your LinkedIn URL.

Tasks

  • Data Storytelling:Develop and present data stories that translate complex analytical results into understandable and actionable insights for non-technical stakeholders
  • Data Analysis and Insight Generation:Analyse large volumes of customer data from various sources (e.g., fuel purchases, convenience store transactions, car services, and EV charging) to uncover actionable business insights
  • Data Wrangling:Clean, pre-process, and transform raw data into structured formats suitable for analysis using SQL and Python
  • Model Development and Validation:Develop, tune, and validate machine learning models to predict customer behaviour. Surface insight that could be actioned to optimise marketing strategies and improve customer retention
  • Visualisation Creation:Design and create compelling data visualisations to communicate findings to stakeholders
  • Ad-hoc Business Queries:Respond to and solve ad-hoc business questions by extracting and analysing relevant data, providing timely and accurate insights
  • Deterministic Record Linking:Employ deterministic record linking techniques, such as blocking, fingerprinting, aggregation, to normalisation, and similarity measures, to match and merge customer records accurately
  • Collaborative Projects:Work collaboratively with cross-functional teams, including technology (e.g., data engineers, analysts, and architecture) and the business (e.g., marketing, operations) to action data-driven insights into business processes
  • Cloud Integration:Use AWS services like Redshift and Athena to manage and analyse large datasets efficiently in the cloud environment

Requirements

  • Senior Data Scientist with over 5 years of experience partnering with customer-focused businesses and renowned global brands, leveraging data-driven storytelling to engage key stakeholders and foster collaboration within dynamic teams
  • Tech required:SQL, Python, Git, AWS (Amazon Redshift, Athena), Jupyter Notebook, Visual Studio Code (the client’s recommended IDE), deterministic record linking techniques, ML model development, good coding best practice
  • Nice-to-have tech:Amazon SageMaker, PySpark, and MLOps



We build and manage your on-demand freelance associate bench via a simple, cost effective service.

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.