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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - Data Quality & Statistical Methodology - **Company:** BLN24 - **Location:** McLean, VA, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Big Data, Python (Programming Language), Reference Data, Standard Sql, SAS (Software), Enterprise Data Management, Cloud Platform System, Apache Spark, Databricks - **Published:** July 11, 2026 - **Apply:** http://bln24.applytojob.com/apply/jobs/details/Kq1YsqI4La ## About the Role * 5+ years of applied experience in statistical methodology, data quality, or quantitative research roles * Demonstrated experience with missing-data and imputation methods (e.g., model-based imputation, hot-deck, sequential regression) for filling incomplete data * Experience designing and validating quantitative metrics for decision-support, including an understanding of bias, variance, and false-positive/false-negative trade-offs * Working knowledge of record linkage / entity resolution concepts, sufficient to build sound metrics on top of matched data * Experience with very large datasets on distributed-compute platforms (e.g., Spark-based / lakehouse environments) and strong SQL * Strong proficiency in Python and R * Comfort working with regulated or restricted data and the governance constraints that accompany it * Strong communication skills and the ability to explain and defend methodology to leadership and non-technical stakeholders, * Master's or PhD in Statistics, Applied Mathematics, Econometrics, Data Science, or a related quantitative field (a purely software-focused background is not sufficient for the methodology components of this role) * Prior experience supporting large-scale enterprise data programs or platform modernization efforts * Experience using external or secondary data to supplement or complete primary datasets * Familiarity with Databricks and modern lakehouse architectures * Exposure to privacy-preserving analytics techniques or working with regulated data * Ability to read and reconcile legacy statistical codebases (e.g., SAS) alongside modern Python/R workflows * Background in requirements gathering for enterprise data platforms * Experience benchmarking estimates against authoritative reference datasets for anomaly or drift detection Work Environment: * Contract position supporting a large-scale enterprise data modernization engagement * Collaborative, cross-functional environment working alongside data engineers, architects, and SMEs * Currently in the requirements-gathering phase of a multi-year platform build - a strong opportunity to shape long-term quality-metric and methodology standards rather than inherit a fixed framework * Must be eligible to work with regulated data and to obtain any background check or clearance required by the client ## Description * Design, define, and validate data-quality metrics for very large datasets - not simply reporting numbers, but establishing what each metric means, how it is calculated, and why it is statistically defensible to leadership * Develop and document methodology for filling gaps where source data is missing, partial, or unreliable, using external and secondary reference data, including model-based and imputation approaches * Establish benchmarking approaches that compare data products against authoritative historical and modeled reference datasets to detect drift, bias, and anomalies * Specify the data the platform must ingest to support quality monitoring, and define the checks that flag when an upstream-produced data product looks wrong * Partner with subject matter experts (SMEs) and stakeholders to translate operational and analytical questions into concrete, measurable quality requirements * Work with data engineers to ensure metrics and gap-filling logic run reliably at scale on very large, multi-source datasets built on common keys and governed definitions * Account for data sensitivity throughout, ensuring appropriate aggregation, access controls, and privacy-preserving techniques are reflected in any metric or derived data product * Document methodology and requirements in structured, reusable formats (e.g., requirements matrices and detailed requirement specifications) * Iterate across multiple review cycles with SMEs and fellow methodologists, given the program's phased, multi-year rollout ## Related Videos - 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