Applied Machine Learning Scientist

Job Description

Job DescriptionQuix Recruitment is partnering with a cutting-edge fintech firm to hire an Applied Machine Learning Scientist to lead the research, development, and deployment of advanced machine learning models that drive innovative solutions in financial markets.

This role is at the intersection of research and engineering, requiring deep expertise in statistical modeling, algorithm development, and hands-on application of machine learning techniques to solve complex real-world problems. You will collaborate closely with quantitative researchers, data scientists, and software engineers to transform theoretical models into scalable, production-ready systems.Key Responsibilities

• Design, develop, and validate novel machine learning and statistical models tailored to financial datasets and market behaviors.
• Conduct rigorous experimentation and hypothesis testing to assess model efficacy and robustness.
• Collaborate cross-functionally to integrate ML models into production trading systems, ensuring performance, scalability, and maintainability.
• Drive innovation by exploring new methodologies including deep learning, reinforcement learning, and unsupervised learning techniques.
• Build and optimize feature engineering pipelines leveraging large-scale, noisy, and high-dimensional financial data.
• Communicate findings and insights clearly to both technical and non-technical stakeholders through reports, presentations, and code documentation.
• Mentor junior team members and contribute to the continuous improvement of modeling and engineering practices.
• Stay at the forefront of ML research by reviewing academic literature, attending conferences, and participating in relevant communities.

Experience & Skills

• Advanced degree (PhD or Master’s) in Computer Science, Statistics, Mathematics, or a related quantitative field with a focus on machine learning or AI.
• 3+ years of experience developing applied ML models in a production environment, preferably within financial services, fintech, or a data-intensive domain.
• Strong programming skills in Python and experience with ML libraries such as TensorFlow, PyTorch, or scikit-learn.
• Expertise in statistical analysis, probabilistic modeling, and evaluation metrics for ML models.
• Hands-on experience with data engineering tools and frameworks for large-scale data processing (Spark, Hadoop, Kafka, etc.).
• Familiarity with cloud infrastructure and containerization technologies for model deployment (AWS, GCP, Docker, Kubernetes).
• Demonstrated ability to translate complex theoretical concepts into practical, business-impacting solutions.
• Excellent communication and collaboration skills within multidisciplinary teams.

Competencies

• Strong analytical thinker with a passion for applied research and innovation.
• Self-driven with the ability to manage multiple projects and deadlines efficiently.
• Detail-oriented and rigorous in validating models and code quality.
• Collaborative mindset committed to sharing knowledge and fostering team growth.

This role offers an exceptional opportunity for applied ML scientists who want to work at the forefront of financial innovation, applying state-of-the-art machine learning to drive competitive advantage in fast-paced, data-rich environments.Confidentiality Statement

At Quix Recruitment, we prioritise confidentiality throughout the recruitment process. We understand the sensitivity of exploring new career opportunities while currently employed. Rest assured that when you apply through Quix Recruitment, your application is kept completely confidential from both your current employer and the hiring company. If we determine that you might be a good fit for the position, we will arrange a confidential call with you to discuss the opportunity in more depth. Only after obtaining your permission will we share your CV with the hiring company.

Disclaimer

Please note that all personal information collected during the application process will be used for recruitment-related purposes only. We are committed to protecting your privacy and will not disclose your information to any third parties without your consent.