Senior Data Scientist

BlueFlag LLP

. Consult with internal clients to frame business problems as analytical or ML problems, define success metrics, and set realistic scope .

Posted 10/6/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Python and SQL for data analysis, with a strong foundation in machine learning methodologies and model evaluation practices. Proven ability to lead AI adoption initiatives and communicate complex technical concepts to diverse audiences.

Highest-signal resume keywords

Python ProficiencyAdvanced SQL SkillsMachine Learning ExpertiseDatabricks ExperienceMLOps Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data AnalysisFeature EngineeringModel DeploymentExperiment TrackingStatistical AnalysisHypothesis TestingRegression AnalysisClusteringAnomaly DetectionCross-Validation

Soft Skills

Communication SkillsClient ConsultationMentoringProblem-SolvingCollaboration

Tools & Technologies

DatabricksMLflowPower BIAzureAWSGCPGitPySparkTableauR

Certifications & Qualifications

Public Trust Clearance

Industry Keywords

AI GovernanceResponsible AIPHI/PII ComplianceFederal Healthcare DataLarge Language Models

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformNumpyPandasPySparkPythonScikit-LearnSparkSQLTableauUnity

About the role

Key responsibilities & impact

Consult with internal clients to frame business problems as analytical or ML problems, define success metrics, and set realistic scope
Build data products and workflows supporting critical operations from source data discovery through production deployment
Migrate and modernize workloads from R, Stata, and SAS to Python and Databricks
Develop, train, and validate classification, regression, forecasting, clustering, and anomaly-detection models
Engineer features and build reusable, governed feature pipelines on large datasets using PySpark and SQL
Track experiments, register models, and manage model versions and promotion through MLflow
Deploy models for batch scoring and real-time serving; automate retraining with scheduled jobs and CI/CD pipelines
Monitor production models for performance, data drift, and data quality; configure thresholds and alerts
Lead client AI adoption, including LLM and agentic use cases such as RAG, document summarization, and classification
Evaluate LLM and agent outputs for accuracy, groundedness, and safety
Document models for governance and responsible AI review
Build reference use cases with tutorials, reference code, and training
Host office hours and pair with client analysts and data scientists to upskill them
Present findings and model results to technical and non-technical audiences, including leadership

Requirements

What you’ll need

Bachelor's degree in Engineering, Computer Science, Statistics, Mathematics, Systems, Business, or a related scientific or technical discipline, and 15+ years of experience (or commensurate experience)
Proficient in Python (pandas, NumPy, SciPy, scikit-learn) and advanced SQL (window functions, CTEs, query tuning) for data analysis
2+ years of hands-on work on a leading cloud data platform such as Databricks, Azure, AWS, or GCP
Experience across the end-to-end data science workflow, from finding and assessing datasets to production deployment, including experiment tracking and model management with MLflow or similar
Solid grounding in traditional machine learning: supervised and unsupervised methods, gradient-boosted trees (XGBoost, LightGBM), model selection, cross-validation, and hyperparameter tuning
Strong applied statistics: hypothesis testing, regression, sampling, and experimental design
Experience working with large datasets in a distributed environment (Spark/PySpark)
Sound model evaluation practice: picking the right metrics, handling class imbalance, avoiding leakage, and explaining model behavior (for example SHAP or feature importance)
Working knowledge of large language models (LLMs) and agentic AI workflows, including prompt design and RAG patterns
Version control with Git and collaborative development practices (code review, branching, testing)
Ability to explain technical work to non-technical stakeholders and turn ambiguous requests into defined deliverables
Must be a citizen of the United States
Must be able to obtain a public trust clearance
Must be eligible to work in the United States
Desired: 5+ years as a data scientist, shipping multiple products that run in operation
Desired: Working experience with Databricks in Azure, including Unity Catalog, Delta Lake, Databricks Jobs, and Databricks notebooks/Repos
Desired: MLOps experience across the lifecycle, including model serving, governed feature tables, CI/CD, production monitoring, model testing, automated retraining, lineage, and auditability
Desired: Experience with Azure AI services or Mosaic AI
Desired: Prior experience shipping products that use LLMs or AI agents, including evaluation and guardrails
Desired: 2+ years building visual insights with Power BI, Databricks AI/BI dashboards, or Tableau
Desired: Prior experience with R, Stata, or SAS
Desired: Experience refactoring R, Stata, or SAS codebases to Python
Desired: Experience with VA or federal healthcare data and handling PHI/PII under federal privacy and security requirements
Desired: Familiarity with federal AI governance and responsible AI practices
Desired: Experience training or mentoring analysts and data scientists
Desired: Master's or PhD in a quantitative field

Benefits

Comp & perks

Competitive salary
Generous annual leave and paid holidays
Comprehensive group health and dental plans
401(k) with company match
Life insurance and AD&D coverage
Ongoing training and professional development opportunities