Data Science Lead
Lead data science strategy and architecture for scalable analytics and ML systems. Design data architectures, evaluate novel data sources, establish analytical methodologies, and bridge R&D to production.
Data Architecture & Scalable Engineering
- Design and oversee the evolution of scalable data architectures that support advanced analytics, ML modeling, and real-time processing.
R&D & Novel Data Source Evaluation
- Scout, evaluate, and pressure-test new internal, external, and alternative data sources (e.g., synthetic data, IoT streams, third-party APIs) for predictive power and commercial viability.
- Lead the ideation and feature engineering for these data sources and document alignment to current and future data architecture designs.
- Lead rapid prototyping and PoCs to validate new technologies, algorithms, and data structures before scaling to production.
- Perform technical vetting of data vendors and partners to ensure data quality, density, and seamless integration capabilities.
Methodology & Analytical Rigor
- Define and document the organization's gold-standard methodologies for statistical analysis, experimental design (A/B testing), and ML modeling.
- Establish rigorous validation protocols and evaluation metrics (e.g., precision/recall, drift detection, bias/fairness audits) to ensure model and data integrity.
- Translate academic research and emerging industry trends into practical business methodologies.
Ingestion & Solution Integration
- Serve as the bridge between R&D and Production, ensuring complex analytical models and data sources are seamlessly ingested into core business products and solutions.
- Oversee data delivery contracts between the DS ecosystem and downstream software applications to ensure clean, well-documented APIs.
Key Deliverables (First 12 Months)
- Data Source Playbook: formalized framework for scoring, vetting, and onboarding new data assets.
- Methodology Registry: centralized repository of approved statistical models, evaluation metrics, and ingestion protocols.
- Feature Importance Registry & Feature Engineering Roadmap: centralized repository connecting current data sources to product value and impact of removal/substitutes.
- Architectural Roadmap: 12-month to 3-year vision aligning data science infrastructure with corporate scaling goals.
Technical Stack
- Python, R, SQL, Cloud Platforms (AWS/GCP/Azure), Big Data tech (Spark, Kafka), Orchestration (Airflow), MLOps tools.
Expertise
- Deep understanding of data modeling, schema design (SQL/NoSQL), statistical evaluation, and MLOps deployment patterns, especially in R&D functions that bridge research with production.
Soft Skills
- Exceptional ability to translate complex technical architectures into strategic business value for non-technical stakeholders.
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