Data Scientist; Looker​/AI​/BI

Opportunity Overview

Northrampis seeking a Data Scientist to join the team supporting a mission-critical effort to consolidate, modernize, andoperateour client'senterprise cloud services across IaaS, PaaS, and SaaS environments.

You will develop analytical models, business intelligence solutions, and AI/ML capabilities that helpthe client derive actionable insight fromitsenterprise 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’sintegrated 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.

Hybrid, based in the Washington, DC metro area. On-site presence at designated client locations is expected on a cadence aligned to program needs. Remote work is supported around mission and security requirements. This role is not open to candidates outside the DC, Maryland, Virginia region.

You turn data into decisions that program managers and agency leaders actually act on.

You know when to reach for a simple statistical model and when the complexity of ML is actually warranted, andyou’vedelivered BI solutions that get used rather than ignored. You communicate findings clearly to non-technicalstakeholdersand youoperatewith rigor around data quality and reproducibility.

• Design, develop, and maintain
  
  LookMLdata models, Looks, and Looker dashboards that deliver actionable business intelligence toclientprogram stakeholders and leadership.

• Build and deploy machine learning models for classification, prediction, anomaly detection, and natural language processing use cases using Python (scikit-learn, Tensor Flow, orPyTorch) and cloud AI/ML services (Vertex AI, Sage Maker, or Azure ML).

• Conduct exploratory data analysis, statistical modeling, and hypothesis testing to surface patterns and insightsinclientoperational and program data.

• Develop andmaintainfeature 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 andclientgovernment 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 andmaintainautomated 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.

• 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, Num Py, scikit-learn,stats models) and SQL for data extraction and analysis.

• Hands-on experience with Looker —LookMLmodeling, dashboard development, and content management.

• Experience training,validating, and deploying machine learning models in cloud environments (Vertex AI, Sage Maker, or Azure ML).

• Strong grounding in statistical methods: regression, classification, time series analysis, and A/B testing.

• Working knowledge ofBigQueryor 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 withMLOpsprinciples: model versioning, experiment…