About the role
Responsibilities:
- Lead and Develop a High-Impact Team: Lead, manage, and mentor a team of data scientists, fostering a culture of continuous learning and professional growth. Guide team members in their project work and support their career development.
- Will own appraisals, compensation queries, and leave management of his direct reports.
- Set Strategic Vision and Drive Innovation: Set clear goals and expectations for the team and individuals, and regularly assess and improve team performance. Ensure alignment with organisational goals and provide technical direction.
- Build Strong Cross-Functional Partnerships: Cultivate trusted relationships with stakeholders in business, product, and engineering to identify opportunities, shape initiatives, and ensure the successful implementation of data science solutions. Act as a bridge to integrate data science seamlessly into business functions.
- Implement Industry-Leading Standards for Model Quality and Monitoring: Define robust metrics and quality standards to evaluate model performance and outcomes. Establish and oversee best-in-class model monitoring systems to ensure reliability, scalability, and sustained impact over time.
Requirements:
- Bachelor'sorMaster's degree in Computer Science, Electrical Engineering, Mathematics, Economics, or another quantitative discipline.
- Demonstrates a research-oriented mindset with 6+ years of experience in data science or related fields.
- Strong foundation in statistics, machine learning (e. g., Random Forests, XGBoost, SVMs), and inference techniques, with expertise in hypothesis testing, simulations, and optimisation methodologies.
- Proficiency in applying deep learning algorithms to solve complex data challenges, with hands-on experience in TensorFlow or PyTorch.
- Skilled in designing, executing, and scaling A/B experiments to validate and optimise data science solutions.
- Advanced Python programming abilities with extensive experience in building robust data pipelines in PySpark, performing feature engineering, and optimising data workflows.
- Deep familiarity with libraries and tools such as pandas, scikit-learn, SQL, and Scala, with additional experience in MLOps frameworks like Docker, Databricks, and Kubernetes for production-grade model deployment.
- Demonstrated track record in developing and implementing large-scale machine learning solutions that drive business impact.
- Experience in leading projects with multiple team members / managing teams is preferred.