Data Scientist (VP)

What You’ll be Doing 

• Develop data science, machine learning, and AI components for automated data ingestion and enrichment pipelines, and rigorous data verification and maintenance processes. 

• Form a deep understanding of Company Intelligence customers, products, and proprietary datasets to ensure solutions are tailored to client needs and market opportunities. 

• Explore third-party datasets to understand and advise on composition, structure, quality, and provenance, with respect to the needs of Company Intelligence customers. 

• Leverage generative and agentic AI to automate complex workflows and enable autonomous decision making, while ensuring robust control, testing, and evaluation mechanisms are in place. 

• Forge constructive business relationships with key stakeholders in Company Intelligence and elsewhere across Preqin & BlackRock. 

• Operate as a “full-stack” Data Scientist – taking projects from problem formulation to production. 

• Exemplify and demonstrate best-practice data science across Preqin & BlackRock. 

• Understand and translate business problems into data science, ML, and AI solutions. 

• Propose smart, pragmatic, and diverse approaches to address a variety of business problems. 

• Determine the value proposition of data science, ML, and AI solutions vs. alternatives. 

• Design and run focused experiments targeting specific business outcomes. 

• Lead individual and group projects, driving them towards the desired outcome. 

• Write quality code to realise models, perform analytics, and draw actionable insights from data. 

• Leverage software development tools and platforms to enable and support solutions. 

• Prioritise and refine your work and tasks relating to the projects you lead and contribute to. 

• Collaborate across Data Science and the broader Engineering division to set and elevate best practices and development standards to increase robustness and reduce time to value.  

• Present results clearly, succinctly, and transparently to a variety of audiences. 

• Use compelling storytelling to contextualize data visualisation and insights, and to inspire action. 

• Embrace a mindset of continuous improvement within Data Science and the broader Engineering division by sharing knowledge, providing feedback, and supporting colleagues. 

• Stay up to date with the latest developments in data science, machine learning & AI, and identify those with business impact. 

Who You Are 

• You approach problems analytically, using evidence and structured thinking to deliver effective data-driven solutions. 

• You take ownership of your work, hold yourself accountable, and are proactive in driving initiatives through to completion. 

• You are committed to measuring the impact of your work and proactively develop feedback loops to inform continuous improvement. 

• You seek to understand business objectives and customer needs, while maintaining the deep technical expertise required to deliver effective, high-quality solutions. 

• You perform well in a fast-paced environment, developing iterative, sustainable solutions, with long-term vision, and exemplifying best practices e.g., security, code quality, documentation.  

• You have a growth mindset; you are curious to discover and excited to learn new technologies, ways of working, and other skills. 

• You are keen to collaborate with technical and non-technical colleagues across Preqin & BlackRock, and with customers.  

Technical Requirements 

• 5+ years’ experience applying data science, machine learning, or AI in a commercial setting. 

• Track record of successfully delivering end-to-end data science, machine learning, or AI solutions. 

• Experience leading technical initiatives, managing direct reports, mentoring, and developing junior data scientists. 

• Intermediate / proficient programming skills in Python & SQL.  

• Proficient user of core Data Science Python libraries including Pandas, NumPy, scikit-learn, Matplotlib or equivalents. 

• Experience using relational database and data warehouse technologies to query for and manipulate data.  

• Proficient knowledge of supervised and unsupervised machine learning, and associated concepts such as model selection, feature engineering, hyperparameter tuning, and evaluation metrics.  

• Proficient user of frameworks such as PyTorch, XGBoost, scikit-learn, or equivalents to develop and optimize machine learning models. 

• Strong data visualisation and storytelling skills using e.g., Metabase, Matplotlib or equivalents. 

• Excellent communication skills to bridge between customers, the business, and technology. 

• Track record of successful collaboration with senior stakeholders and cross-functional teams. 

• Experience with MLOps practices including model deployment, monitoring, lifecycle management, and experiment tracking using MLFlow or equivalent. 

• Experience designing and architecting scalable and maintainable data science and ML systems. 

• Experience using a cloud computing platform such as AWS, Azure, GCP or equivalent. 

• Secure knowledge of software engineering practices such as CI / CD pipelines, containerization (Docker, Kubernetes etc.), collaborative development using Git, testing, and code quality assessment. 

Desirable 

• Experience developing production-quality solutions leveraging generative or agentic AI. 

• Experience developing scalable data pipelines and transforming data using tools such as dbt. 

• Experience leveraging and / or fine-tuning pre-trained models such as BERT. 

• Awareness of data ethics, responsible AI, and data governance (e.g. privacy, bias mitigation, compliance). 

• Working knowledge of the financial services sector and alternative assets e.g., private equity, real estate, and infrastructure.