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.
179k – 200k
Remote6+ YOEData Science
About the role
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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