Team Lead Data Intelligence

Job Summary:

The Team Lead Data Intelligence will be a key member of the Category Management Operations, responsible for leading and driving advanced analytics, machine learning, and data science initiatives supporting LKQs Europes CATMAN strategic priorities.

This role will combine hands-on technical expertise with team leadership, guiding a team of data scientists and analysts to deliver high-quality, scalable solutions. The individual will be responsible for identifying opportunities to leverage data science for business value creation, improving data quality, and enabling data-driven decision-making across customers, competitors, and market trends. The role requires strong collaboration with European Category Management, Pricing, and IT teams, ensuring alignment with business objectives and delivering impactful insights through AI/ML applications.

Essential Job Duties:

Leadership & Team Management

• Lead, mentor, and develop a team of data scientists and analysts
• Set clear goals, performance expectations, and career development plans
• Drive a high-performance, collaborative, and innovation-focused team culture
• Ensure adherence to best practices in data science, coding standards, and documentation
• Build team capability across business understanding, data storytelling, stakeholder management etc.

Data Science & Advanced Analytics

• Design and deploy advanced machine learning models (regression, classification, clustering, time-series, NLP)
• Own end-to-end lifecycle of data science projects
• Drive adoption of AI/ML across key business use cases
• Ensure scalability, reliability, and maintainability of models in production

Business Impact & Strategy

• Partner with stakeholders to translate business problems into analytical solutions
• Drive strategic initiatives such as:
• Demand forecasting
• Replacement Rate modeling
• Anomaly detection and automation

Reporting & Analytics Delivery:

• Ensure standardization, KPI definitions, semantic models aligned with enterprise data standards
• Focus/enable team on report reusability, performance optimization and user adoptions
• Promote automation of repetitive tasks to enhance productivity
• Collaborate with IT on scalable data architecture (e.g., MS Fabric environment)

Innovation & Continuous Improvement

• Drive POCs and experimentation to evaluate new tools, models, and techniques
• Stay updated on emerging technologies in AI/ML and analytics
• Introduce best practices in model governance, explainability, and monitoring
• Continuously identify opportunities to improve processes and data quality.

Basic Qualifications: Masters degree in data science, Computer Science, Business Analytics, Statistics, or related field (MBA with strong analytics focus also acceptable)

Experience: 610 years of experience in data science roles, 2+ years of experience in leading or mentoring teams, Proven experience in delivering end-to-end machine learning projects in a business environment;

Knowledge/Skills/Abilities:

• Advanced SQL skills including complex query design, performance tuning, and stored procedure development
• Strong knowledge of relational and dimensional modeling; ability to translate business processes into entities, facts, dimensions, and KPIs.
• Strong programming skills in Python / R; Experience with ML libraries: scikit-learn, TensorFlow, PyTorch
• Expertise in: Time-series analysis, Forecasting models, Anomaly detection, NLP etc.
• Experience with Power BI / Tableau / Qlik; Strong knowledge of data wrangling, feature engineering, and model optimization
• Exposure to Microsoft Fabric / modern data platforms is a plus

Preferred Qualifications:

• Strong experience in end-to-end AI/ML lifecycle management, including model development, validation, deployment, monitoring, and continuous improvement, aligned with structured workflows used in analytics and CoE initiatives.
• Hands-on exposure to MLOps practices, including:
• Model versioning and experiment tracking
• CI/CD pipelines for ML deployments
• Monitoring model performance, drift detection, and retraining strategies
• Ensuring production stability through alerts, retries, and SLA-based monitoring.
• Ability to translate analytical models into deployable and scalable business solutions, consistent with production-oriented model deployment approaches
• Ability to integrate ML outputs with BI tools (Power BI / Tableau) for business consumption, ensuring insights are actionable and decision-ready, consistent with LKQ reporting practices

Personal Attributes:

• Resilient and results-driven, with a strong can-do attitude and the ability to navigate ambiguity in complex data science and AI problem spaces.
• Proven ability to manage and prioritize multiple concurrent projects, balancing experimentation, model development, and production deliverables effectively.
• Strong collaboration mindset, with the ability to work seamlessly across cross-functional teams (business, IT, data engineering, and analytics).
• Self-starter with high ownership, demonstrating initiative in identifying opportunities for AI/ML-driven value creation and driving them end-to-end.
• Excellent problem-solving and analytical thinking skills, with the ability to debug models, identify data issues, and optimize processes for scalability and efficiency.
• Continuous learner with a strong inclination toward innovation, experimentation, and adoption of emerging AI/ML practices.