Data Scientist

Randstad Sourceright
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Duration:6-month contract position

Rate:inside IR35

Hours:37.5 hours per week.

Location:Remote, option to go into the office once a month

About the Role:

This exciting opportunity with one of our clients who is a globally recognised name within food and nutrition is for a Senior Data Scientist to join the Talent Management Center of Excellence (COE) at a leading global organization. The COE is dedicated to attracting and retaining top talent, focusing on career development, performance management, leadership development, and manager capability. This role will significantly impact organizational success by leveraging data insights to enhance the manager experience and overall productivity.

Key Responsibilities:

  • Data Collection and Processing: Gather, clean, validate, and prepare data from diverse sources including surveys, focus groups, reports, databases, and feedback platforms for in-depth analysis.

  • Data Analysis: Employ advanced statistical methods and data visualization to interpret data, uncover trends, and create actionable insights. Lead the development of analytical approaches, collaborating with data engineers, business leaders, and developers to build robust, scalable, and easily interpretable data models.

  • Reporting and Visualization: Create clear, concise reports and dashboards utilizing tools like Power BI, REACT, or Excel. Work collaboratively with the Talent Marketplace adoption manager to refine and optimize existing reports.

  • Collaboration: Partner closely with cross-functional teams (Segment Talent, P&O Business Partners, MGS reporting, Culture COE) to understand their data requirements and deliver impactful, data-driven solutions. Actively participate in projects focused on improving manager satisfaction and engagement.

  • Communication: Effectively communicate data findings, analytic approaches, and their implications to business partners. Advocate for data-driven decision-making and clearly explain complex analysis.

  • Process Improvement: Analyze processes and identify areas for optimization based on data insights to improve manager experience.

  • Data System Management: Maintain data integrity, ensure efficient data systems operations, and leverage your understanding of data architecture to build innovative features combining both internal and external data sources.

  • Predictive Analysis: Utilize predictive modeling techniques to anticipate future trends and recommend data-driven strategies to guide HR initiatives.

Qualifications:

Experience:

  • Proven experience in a data science role or comparable experience.

  • Expertise in statistical modeling (e.g., significance testing, GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis) using tools such as Spark, Scala, SAS, R, Python, Bayesia, H2O, Storm, Yarn, and Kafka.

  • Proven experience querying databases using SQL and Hive.

  • Hands-on experience working with large datasets using big data platforms like Hadoop ecosystem (Azure), and in-memory solutions (SAP HANA and Apache Spark).

  • Proficient in data visualization tools such as Tableau, Power BI, D3, or ggplot.

Skills:Data Science, Algorithms, Data Analysis, NLP, Statistics, Data Visualization, Project Management, Planning & Organizing, Document Preparation

Competencies:Ensures Accountability, Plans and Aligns, Action-Oriented, Tech Savvy, Business Insight, Optimizes Work Processes, Cultivates Innovation, Drives Engagement, Manages Complexity, Situational Adaptability

DE&I

We are committed to providing equal employment opportunities and encourage all qualified candidates to apply. While the hiring process may not be expedited, we urge all interested candidates to submit their applications promptly to ensure their consideration.

#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 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.