Data Scientist (Looker / AI / BI)

Northramp LLC•Washington, DC

4d•Hybrid

About The Position

Northramp is seeking a Data Scientist to join the team supporting client's Cloud BPA Bridge program — a mission-critical effort to consolidate, modernize, and operate client's enterprise cloud services across IaaS, PaaS, and SaaS environments under FedRAMP High authorization. You will develop analytical models, business intelligence solutions, and AI/ML capabilities that help client derive actionable insight from its enterprise data. The role spans statistical analysis, machine learning, and data visualization — with Looker as the primary BI delivery platform — in support of program operations, resource planning, and mission-critical decision-making. This role is part of Northramp’s integrated delivery model, where engineers and advisors work as one team to bring sound judgment, disciplined execution, and deep federal experience to high-stakes modernization programs.

Requirements

3 to 6 years of progressive, hands-on experience in data science or applied analytics with production model deployment experience.
Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field.
Proficiency in Python for data science (pandas, NumPy, scikit-learn, statsmodels) and SQL for data extraction and analysis.
Hands-on experience with Looker — LookML modeling, dashboard development, and content management.
Experience training, validating, and deploying machine learning models in cloud environments (Vertex AI, SageMaker, or Azure ML).
Strong grounding in statistical methods: regression, classification, time series analysis, and A/B testing.
Working knowledge of BigQuery or equivalent cloud data warehouses for large-scale analytical workloads.
Experience with data visualization best practices and BI tooling beyond Looker (e.g., Tableau, Power BI, or Google Looker Studio).
Familiarity with MLOps principles: model versioning, experiment tracking (MLflow or Vertex AI Experiments), and deployment pipelines.
Understanding of federal AI governance guidance (OMB M-24-10 or equivalent) and FedRAMP data handling requirements.
U.S. Citizenship and the ability to obtain and maintain a DHS suitability / Public Trust clearance.

Nice To Haves

Google Cloud Professional Machine Learning Engineer or equivalent AWS/Azure ML certification.
Looker certification or demonstrated advanced LookML experience.
Security+ or equivalent certification.
Experience with NLP, computer vision, or generative AI application development.
Federal data science or analytics program experience.
Active Public Trust or higher clearance.

Responsibilities

Design, develop, and maintain LookML data models, Looks, and Looker dashboards that deliver actionable business intelligence to client program stakeholders and leadership.
Build and deploy machine learning models for classification, prediction, anomaly detection, and natural language processing use cases using Python (scikit-learn, TensorFlow, or PyTorch) and cloud AI/ML services (Vertex AI, SageMaker, or Azure ML).
Conduct exploratory data analysis, statistical modeling, and hypothesis testing to surface patterns and insights in client operational and program data.
Develop and maintain feature engineering pipelines, model training workflows, and model serving infrastructure integrated with cloud data platforms and BigQuery.
Partner with Data Engineers to define data requirements, validate pipeline outputs, and ensure analytical datasets meet quality and completeness standards.
Collaborate with program leadership and client government stakeholders to translate mission requirements into analytical problem definitions and measurable KPIs.
Implement responsible AI practices — model explainability, bias assessment, and documentation standards — consistent with federal AI governance frameworks.
Build and maintain automated reporting and alerting workflows that surface operational metrics and anomalies to the right stakeholders at the right time.
Document data science methodologies, model assumptions, validation results, and performance metrics to support ATO and audit requirements.
Mentor junior analysts and support adoption of data-driven practices across the delivery team.