Data Scientist developing network - based financial - crime detection for TD Bank. Building models, visualizations, and decision - support tools to prioritize high - risk customer connections.
About the role
- Senior Data Scientist leading deep analytical investigations to transform complex datasets into actionable insights. Collaborating with cross-functional teams on high-value analytic requests for operational needs.
Responsibilities
- Design and lead investigations into patterns, trends, and edge cases across filtered datasets.
- Develop interaction models and fused analyses across multiple entity types and data modalities.
- Design data validation, anomaly sanity checks, and analytical reliability frameworks to ensure analytical outputs behave correctly across varied data inputs.
- Partner with solutions and data engineering to embed analytic logic into data pipelines and services.
- Conduct bespoke, high-complexity analysis in support of customer-facing or operational needs.
- Guide team best practices in Spark SQL usage, data documentation, and exploratory reproducibility.
Requirements
- 5+ years of experience in data science, applied analytics, machine learning, or analytical R&D.
- Advanced expertise in Python and distributed compute frameworks (e.g., Spark, Databricks), including strong proficiency in Spark SQL.
- Strong background in statistical inference, anomaly detection, clustering, interaction modeling, or other analytical methods suited to large and heterogeneous datasets.
- Experience working with multi-source, semi-structured, geospatial, or entity-centric data, with a strong ability to derive insight from complex operational environments.
- Demonstrated success building data quality, validation, or reliability frameworks, particularly for analytical workflows or model-adjacent processes.
- Ability to translate ambiguous analytical problems into structured, reproducible investigation plans.
- Excellent communication, mentorship, and cross-functional collaboration skills.
- Nice to have: Experience with MLflow, feature stores, or MLOps platforms; familiarity with model lifecycle management, reproducibility tooling, or production model monitoring.
Benefits
- Competitive salary
- Comprehensive health, dental, and vision insurance plans
- Flexible hybrid work environment
- Additional benefits like flexible hours, work travel opportunities, competitive vacation time and parental leave
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