Principal Data Scientist

Basford
3 months ago
Applications closed

Related Jobs

View all jobs

Principal Bioinformatician

Senior Data Scientist (MLOps)

Principle Engineer

Principal Data Engineer

Principal Enterprise Architect (Data & Customer)

Principal Enterprise Architect (Data & Customer)

Principal Data Scientist
Location: Nottinghamshire (Hybrid 3days/week)
Salary: £80,(Apply online only) – £90,(Apply online only)

Are you ready to take the lead in shaping the future of data science? We are recruiting on behalf of an exciting organisation for a Principal Data Scientist role based in Nottinghamshire. This is a hybrid position that offers the chance to combine hands-on technical work with strategic leadership, driving transformative business outcomes through data.

You’ll oversee the development of advanced analytics, machine learning models, and AI-driven solutions while collaborating with cross-functional teams to solve complex business challenges. This opportunity is the first lead hire for our clients data team so an exciting opportunity to make an impact.

Key Responsibilities

Shape and implement the organisation’s data science strategy, ensuring alignment with business objectives.
Provide thought leadership in machine learning and advanced analytics, guiding teams and influencing decision-making at the executive level.
Stay ahead of industry trends, positioning the organisation as a leader in data-driven innovation.

Design and deliver machine learning models, optimisation algorithms, and predictive analytics tools.
Lead full lifecycle projects, from data collection and preprocessing to model development, testing, and deployment.
Explore and deploy cutting-edge methods, including deep learning, natural language processing (NLP), and reinforcement learning.

Partner with data engineering teams to create scalable and reliable data pipelines.
Work closely with product, engineering, and business units to identify and capitalise on opportunities for innovation.
Simplify complex data science findings into actionable insights for diverse stakeholders.

What We’re Looking For

Master’s or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, or a related discipline.
Experienced in data science or machine learning, with proven experience leading impactful projects.
Expertise in driving data science initiatives with measurable business outcomes.
Technical Skills:

Strong proficiency in machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Advanced knowledge of programming languages such as Python, R, or Scala.
Experience working with large-scale data platforms (e.g., Spark, Snowflake, Hadoop).
Hands-on expertise with cloud services like AWS, Azure, or GCP.
Strong analytical skills with a deep understanding of statistics and optimisation techniques.
Outstanding communication skills to bridge technical concepts and business objectives effectively.
Preferred Qualifications

Experience with advanced AI techniques such as NLP, deep learning, or reinforcement learning.
Knowledge of MLOps principles and tools (e.g., MLflow, Kubeflow).
Demonstrated contributions to peer-reviewed publications or open-source projects.

For more information on this role or other similar roles please contact Mae Fitzgerald

Xpertise are acting as an employment agency and business

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.