Data Science Team lead

About BLeader

BLeader is a leading Data & AI consulting and implementation company, founded in 2014. We specialize in guiding organizations through their data journey — from strategy and infrastructure to advanced AI solutions — with a focus on delivering measurable business results. As part of the Octally Group and a strategic partner of KPMG, we bring broad capabilities and enterprise-grade credibility to every project.

About the Role

We’re looking for an experienced Data Science Team Lead to join BLeader and lead a dedicated team working with one of our key clients — a major financial services company specializing in credit solutions. You’ll be driving ML initiatives and shaping their AI strategy.

What you’ll do

∙ Lead and mentor a team of data scientists, setting technical direction and priorities

∙ Design and deliver ML models for credit risk assessment, fraud detection, underwriting, and customer analytics

∙ Drive the adoption of GenAI capabilities within the client’s organization

∙ Collaborate with product, engineering, and business stakeholders to translate complex requirements into data-driven solutions

∙ Establish best practices for model development, validation, and deployment

∙ Serve as the bridge between BLeader’s expertise and the client’s business needs

What we’re looking for

∙ 5+ years of hands-on experience in data science and machine learning

∙ 2+ years of experience managing a data science team (minimum 3 direct reports)

• 3+ Years in the financial industry

∙ Deep expertise in ML techniques including classification, regression, ensemble methods, and time series analysis

∙ Strong proficiency in Python and ML frameworks (scikit-learn, XGBoost, LightGBM)

∙ Experience with GenAI tools and LLM applications in business contexts

∙ Excellent communication skills with ability to present to senior leadership

Nice to have

∙ Previous experience in financial services, credit, or lending

∙ Familiarity with regulatory requirements in the financial sector (model governance, explainability)

∙ Experience with cloud ML platforms (Azure ML, AWS SageMaker, Databricks)