Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Head of Data Science Engineering

Jobleads
London
7 months ago
Applications closed

Related Jobs

View all jobs

Head of Data Science

Head of Data Science

Head of Data Science

Head Of Data Science

Head of Data Science Data Analytics Business Intelligence · London ·

Head of Data Engineering

Job Description

An exciting opportunity has arisen for an experienced data leader to drive innovation and enhance data-driven decision-making at a leading private equity firm. This role will spearhead the development of cutting-edge data solutions, leveraging advanced analytics, predictive modeling, and machine learning to optimize investment strategies and operational efficiency.

The Role

As Head of Data , you will be responsible for shaping and executing the firm’s data strategy, working closely with stakeholders across technology, investment, and transformation teams.

Your expertise in data engineering, analytics, and machine learning will play a pivotal role in building scalable data solutions, refining governance frameworks, and enhancing analytical capabilities.

Key Responsibilities

  • Collaborate with senior leadership to refine and implement the firm’s data science strategy, aligning it with broader business priorities.
  • Design and develop data platforms, pipelines, and analytical tools that support investment decision-making and risk management.
  • Drive innovation by applying advanced machine learning techniques, AI, and predictive modeling to private markets investment challenges.
  • Oversee data governance, ensuring high-quality, structured, and unstructured data is effectively managed and utilized.
  • Enhance reporting and analytics capabilities, creating intuitive dashboards and user-centric analytical solutions.
  • Lead a high-performing data team, providing mentorship, professional development, and fostering a culture of continuous improvement.
  • Monitor industry trends, regulatory developments, and emerging technologies to keep the firm at the forefront of data innovation.
  • Establish KPIs to measure the success of data initiatives and provide insights to senior leadership.

Requirements

  • Experience in data science, data engineering, or analytics, ideally within investment management, financial services, or private markets.
  • Strong technical expertise in data architecture, data lakes, and cloud platforms, including experience with machine learning frameworks (TensorFlow, PyTorch, Hugging Face) and big data processing (Spark, Synapse).
  • Proven track record of leading high-performing teams and driving data-led transformation within a complex organization.
  • Strong strategic mindset with the ability to translate data insights into actionable business outcomes.
  • Excellent communication skills, with the ability to influence senior stakeholders and drive cross-functional collaboration.

This is a unique opportunity to shape the future of data science and engineering within a dynamic investment environment. If you’re a forward-thinking data leader looking to make a meaningful impact, I’d love to hear from you.

#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.

Jobs

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Jobs

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Jobs

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.

🍪 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.