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

Apply Now

Data Scientist - Up to £150k

ZipRecruiter
London
1 month ago
Create job alert

Job Description

Department: Information Technology

Job Summary

We are seeking a highly skilled and motivated Data Scientist with a strong focus on machine learning (ML) and artificial intelligence (AI) to join our innovative team. The ideal candidate will excel in developing, deploying, and optimizing ML models and AI solutions, leveraging cutting-edge technologies like Azure Machine Learning, AutoML, and cloud-based AI tools. This role is essential for driving insights, automating processes, and delivering impactful business outcomes.

Key Responsibilities

ML and AI Model Development

  • Design, build, and deploy machine learning models using advanced algorithms, including AutoML techniques.
  • Develop and fine-tune models for regression, classification, clustering, and deep learning.
  • Explore and implement state-of-the-art AI approaches such as transformer models, generative AI, and reinforcement learning.
  • Expertise in natural language processing to analyze and generate insights from unstructured text data.

Advanced Analytics and Insights

  • Extract actionable insights from structured and unstructured datasets to support strategic decision-making.
  • Use predictive and prescriptive analytics to solve business challenges.

Technology and Tools

  • Utilize Azure Machine Learning and AI tools to manage model lifecycles.
  • Leverage cloud platforms like Azure, AWS, and GCP for scalable ML model deployment.
  • Employ frameworks like TensorFlow, PyTorch, and scikit-learn for model development.

Data Engineering and Preparation

  • Oversee data ingestion, cleaning, transformation, and feature engineering processes to ensure high-quality datasets.
  • Work with large datasets and implement scalable data pipelines.

Model Evaluation and Optimization

  • Evaluate model performance using metrics such as R2, RMSE, ROC-AUC, F1 score, and precision-recall.
  • Optimize models through hyperparameter tuning, feature selection, and iterative testing.

Collaboration and Deployment

  • Partner with cross-functional teams to integrate ML solutions into business applications.
  • Build and maintain APIs for deploying AI solutions at scale.

Documentation and Best Practices

  • Document all ML/AI processes and maintain a centralized repository.
  • Establish and follow best practices in model versioning, reproducibility, and model governance.

Qualifications

Education

Advanced degree (Master’s or Ph.D.) in Computer Science, Data Science, Statistics, or a related field. Equivalent experience will be considered.

Experience

Minimum of 5 years in a data science or machine learning-focused role.

Technical Skills

  • Expertise in designing and deploying ML algorithms, AutoML tools, and AI applications.
  • Proficiency with programming languages such as Python and R, and ML libraries (TensorFlow, PyTorch, scikit-learn).
  • Hands-on experience with cloud platforms (Azure ML) and big data ecosystems (e.g., Hadoop, Spark).
  • Strong understanding of CI/CD pipelines, DevOps practices, and infrastructure automation.
  • Familiarity with database systems (SQL Server, Snowflake) and API integrations.
  • Strong skills in ETL processes, data modeling, and DAX.
  • Experience with BI tools like Power BI or Tableau is a plus.
  • Knowledge of advanced ML techniques such as federated learning and generative adversarial networks (GANs).

Soft Skills

  • Analytical mindset with exceptional problem-solving abilities.
  • Excellent communication and collaboration skills to work in cross-functional teams.
  • Self-motivated with attention to detail and a commitment to continuous learning.
  • Ability to research and discuss complex technical topics when working with other teams.

Working Conditions
Office environment with moderate noise level; able to work flexible hours if needed.

Conner Strong & Buckelew is proud to be an equal opportunity employer. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, ancestry, disability (physical or mental), marital or domestic partnership or Civil Union status, genetic information, atypical cellular or blood trait, military service or any other status protected by law.

#LI-HYBRID


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist - Hybrid

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.

The Future of Machine Learning Jobs: Careers That Don’t Exist Yet

Machine learning (ML) has become one of the most powerful forces reshaping the modern world. From voice assistants and recommendation engines to fraud detection and medical imaging, it underpins countless applications. ML is no longer confined to research labs—it powers business models, public services, and consumer technologies across the globe. In the UK, demand for machine learning professionals has risen dramatically. Organisations in finance, retail, healthcare, and defence are embedding ML into their operations. Start-ups in Cambridge, London, and Edinburgh are pioneering innovations, while government-backed initiatives aim to position the UK as a global AI leader. Salaries for ML engineers and researchers are among the highest in the tech sector. Yet despite its current importance, machine learning is only at the beginning of its journey. Advances in generative AI, quantum computing, robotics, and ethical governance will reshape the profession. Many of the most vital machine learning jobs of the next two decades don’t exist today. This article explores why new careers will emerge, the roles likely to appear, how today’s roles will evolve, why the UK is well positioned, and how professionals can prepare now.

Seasonal Hiring Peaks for Machine Learning Jobs: The Best Months to Apply & Why

The UK's machine learning sector has evolved into one of Europe's most intellectually stimulating and financially rewarding technology markets, with roles spanning from junior ML engineers to principal machine learning scientists and heads of artificial intelligence research. With machine learning positions commanding salaries from £32,000 for graduate ML engineers to £160,000+ for senior principal scientists, understanding when organisations actively recruit can dramatically accelerate your career progression in this pioneering and rapidly evolving field. Unlike traditional software engineering roles, machine learning hiring follows distinct patterns influenced by AI research cycles, model development timelines, and algorithmic innovation schedules. The sector's unique combination of mathematical rigour, computational complexity, and real-world application requirements creates predictable hiring windows that strategic professionals can leverage to advance their careers in developing tomorrow's intelligent systems. This comprehensive guide explores the optimal timing for machine learning job applications in the UK, examining how enterprise AI strategies, academic research cycles, and deep learning initiatives influence recruitment patterns, and why strategic timing can determine whether you join a groundbreaking AI research team or miss the opportunity to develop the next generation of machine learning algorithms.

Pre-Employment Checks for Machine Learning Jobs: DBS, References & Right-to-Work and more Explained

Pre-employment screening in machine learning reflects the discipline's unique position at the intersection of artificial intelligence research, algorithmic decision-making, and transformative business automation. Machine learning professionals often have privileged access to proprietary datasets, cutting-edge algorithms, and strategic AI systems that form the foundation of organizational competitive advantage and automated decision-making capabilities. The machine learning industry operates within complex regulatory frameworks spanning AI governance directives, algorithmic accountability requirements, and emerging ML ethics regulations. Machine learning specialists must demonstrate not only technical competence in model development and deployment but also deep understanding of algorithmic fairness, AI safety principles, and the societal implications of automated decision-making at scale. Modern machine learning roles frequently involve developing systems that impact hiring decisions, financial services, healthcare diagnostics, and autonomous operations across multiple regulatory jurisdictions and ethical frameworks simultaneously. The combination of algorithmic influence, predictive capabilities, and automated decision-making authority makes thorough candidate verification essential for maintaining compliance, fairness, and public trust in AI-powered systems.