Remote Data Scientist: AI/BI & Looker

Northramp LLC

Bethesda (MD)

Hybrid

USD 120,000 - 160,000

Full time

14 days+

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Benefits offered by this job

Health Care Plan

Retirement Plan

Life Insurance

Paid Time Off

Family Leave

Disability Insurance

Training & Development

Work From Home

Wellness Resources

Employee Bonus Programs

Job summary

Northramp LLC seeks a Data Scientist to support a federal Cloud Modernization program. You will develop BI solutions and AI/ML capabilities with Looker/LookML, Python, and cloud services to derive actionable insights across enterprise data.

The ideal candidate will balance statistical analysis, ML, and data visualization, delivering robust data-driven decisions for program operations and leadership while adhering to federal governance standards and security requirements. Remote/hybrid in DC area.

Qualifications

3 to 6 years of progressive, hands-on data science or analytics with production model deployment experience.
Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, CS, or a related quantitative field.
Proficiency in Python for data science and advanced SQL for complex transformations and large-scale analysis.
Hands-on expertise with Looker/LookML, scalable semantic models, PDTs, and version-control.
Experience with ML model development and deployment on Vertex AI, SageMaker or Azure ML.
Knowledge of LookML dashboards, data visualization best practices, and BI tooling beyond Looker.

Responsibilities

Design, develop, and maintain LookML data models, Looks, and LookML dashboards for actionable BI.
Build and deploy ML models for classification, prediction, anomaly detection, and NLP using Python and cloud services.
Conduct exploratory data analysis, statistical modeling, and hypothesis testing on client data.
Develop feature engineering pipelines, training workflows, and model serving with cloud data platforms and BigQuery.
Collaborate with Data Engineers to define data requirements and ensure data quality.
Translate mission requirements into analytical KPIs and collaborate with stakeholders.
Implement responsible AI practices including model explainability and bias assessment.
Create automated reporting and alerting workflows for operational metrics.
Document methodologies, validation results, and performance metrics for audits.
Mentor junior analysts and promote data-driven practices across the team.