Lead Data Science Consultant

Overview

In this role you will lead data science consultancy for major financial software clients, building predictive analytics and models across credit risk, fraud, collections and marketing. You will head the team defining the Big Data strategy and contribute to R&D initiatives in the digital space. You’ll work with business SMEs to translate needs into actionable data architectures and production-ready solutions. This position blends hands-on modeling with strategic guidance, in a fast, tech-forward environment near Wimbledon, London.

Pay / Benefits

• relaxed open environment
• casual dress code

Responsibilities

• Lead the team defining the Big Data strategy for the client
• Develop predictive analytics and ML models across credit risk, fraud, collections, and marketing
• Lead or participate in R&D projects in the digital space and explore new tools and methods
• Build relationships with business SMEs and stakeholders to understand requirements
• Extract and present data from diverse, unclean sources
• Provide innovative data-centric solutions and strong architectural designs
• Consult on data management capabilities and enterprise data concepts
• Advise on data architecture, KPIs, and reporting outputs
• Deliver production ML solutions and support ongoing deployments

Key requirements

• Professional experience of more than 2 years
• Strong Data Science background with probability and statistics knowledge
• Experience implementing ML/data science solutions for corporate clients
• Ability to face clients and consult; strong business acumen
• Experience delivering production solutions, not just analyses
• Ideally experience with ML techniques such as linear/logistic regression, boosting, kNN
• Ideally Python or another programming language
• Experience with frameworks (TensorFlow, scikit-learn, Theano, Caffe, Torch)
• Knowledge of data architecture and data management capabilities
• Client-facing communication
• Consultancy and problem-framing of ambiguous requirements
• Cross-industry business acumen
• Machine learning techniques (linear/logistic regression, boosting, kNN)
• Python or another programming language
• Frameworks: TensorFlow, scikit-learn, Theano, Caffe, Torch

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