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Senior Data Scientist — Data Quality & Statistical Methodology

BLN24

Role overview

Qualifications

  • 5+ years of applied experience in statistical methodology, data quality, or quantitative research roles
  • Demonstrated experience with missing-data and imputation methods for filling incomplete data
  • Experience designing and validating quantitative metrics for decision-support
  • Strong proficiency in Python and R

Responsibilities

  • Design, define, and validate data-quality metrics for very large datasets
  • Develop and document methodology for filling gaps where source data is missing
  • Establish benchmarking approaches that compare data products against authoritative datasets
  • Partner with SMEs and stakeholders to translate operational questions into measurable quality requirements

About the company

BLN24 logo

BLN24

BLN24 executes a different approach to solve communications, technology, data, and security challenges. We solve the governments biggest problems with transparency and honesty. BLN24 focuses on breaking down silos to streamline mission outcomes; removing unnecessary complexity; developing communication and interfaces to better collaborate; optimizing value in investments.

Company details

Company typeStartup
Company size11 - 50

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Job description

Job Title: Senior Data Scientist — Data Quality & Statistical Methodology

Company: BLN24

About Us: We find strength in teamwork-a better you is a better us
BLN24 is an award-winning Management Consulting Firm that supports the U.S. Federal Government in successfully achieving their mission and goals. Our service and solutions delivery start with understanding each client’s end-state, and then seamlessly integrating within each Agency’s organization to improve and enhance strategic and technical operations and deployments.

Position Overview:
BLN24 is seeking a Senior Data Scientist with a strong statistical-methodology focus to support a large-scale enterprise data and analytics platform modernization effort. This role sits at the intersection of statistical methodology, data-quality measurement, and large-scale data engineering — designing the metrics and methods that determine whether an organization’s core data products can be trusted.

A central challenge is completeness: source data is rarely perfect, and values are routinely missing, partial, or unreliable. The ideal candidate understands how external and secondary data sources can be used to responsibly fill those gaps, and can design defensible, statistically sound quality metrics that measure how well the resulting data reflects reality.

The platform’s anticipated foundation involves a modern lakehouse/cloud data architecture handling very large datasets from multiple providers. The successful candidate will help define the quality-metric framework and gap-filling methodology for a generation of stakeholders moving off legacy tools and fragmented, manually validated processes.

Key Responsibilities:
  • 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
     
Required Qualifications:
  • 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

Preferred Qualifications:
  • 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
     
What BLN24 brings to the Game:
BLN24 benefits are game changing. We like our team to play hard and that means they need to be taken care of — physically, financially, and emotionally. We make sure to keep them in the game by giving them access to generous medical, dental, and vision plans.
  • You can join one of the fastest growing companies headquartered in the Washington DC Metro Area.  We give you the opportunity to work in different sectors, so you have the chance at variety while maintaining stability.
  • Flexibility at BLN24 allows each individual the opportunity to balance quality work and their personal lives. Depending on projects, we allow remote working opportunities so you can always be in the game no matter where you call home.
BLN24 is an Equal Opportunity Employer. We believe people are our strength and understand diverse talents are key to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as any mental health or physical disability needs.

 

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Marcus Rivera

Chief Revenue Officer

m.rivera@company.com
linkedin.com/in/marcusrivera
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