Data Scientist, Databricks

Guidehouse

Databricks Data Scientist developing machine learning models and insights supporting client projects. Collaboration across teams and strong Python/SQL skills required for this role.

Posted 7/28/2026full-timeRemote • Colorado, District of Columbia, Illinois, Massachusetts, New York, Texas, Virginia • 🇺🇸 United StatesMid-LevelSenior💰 $113,000 - $188,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing, training, and evaluating machine learning models using Python and SQL, while leveraging Databricks, Spark, and Delta Lake for data processing and analysis. Proficient in model governance practices, data validation, and effective communication of analytical insights to diverse audiences.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingSQL Data AnalysisDatabricks Platform ExperienceML Lifecycle Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData AnalysisFeature EngineeringModel EvaluationData PreparationModel DeploymentExperiment TrackingVersion ControlData ValidationStatistical Modeling

Soft Skills

Effective CommunicationTeam CollaborationProblem SolvingAnalytical ThinkingTechnical Troubleshooting

Tools & Technologies

DatabricksSparkDelta LakeMLflowUnity Catalog

Industry Keywords

Data GovernanceAI GovernanceCloud-Native Data PlatformsOperational WorkflowsBusiness Analytics

Tech Stack

Tools & technologies

CloudPythonSparkSQLUnity

About the role

Key responsibilities & impact

Develop, train, and evaluate machine learning and statistical models to support business and mission needs using the Databricks platform
Prepare, clean, and maintain datasets for modeling, experimentation, and analysis
Write, optimize, and maintain Python and SQL workflows for data exploration, feature engineering, and model development
Work with large-scale datasets using Databricks, Spark, and Delta Lake platforms
Design reusable feature engineering workflows and model training pipelines using Databricks notebooks, workflows, and MLflow
Register, version, promote, and document models using MLflow Model Registry and Unity Catalog-based model governance practices
Monitor deployed models for performance, drift, data quality, usage patterns, and operational issues; recommend retraining, tuning, or retirement actions as needed
Analyze data to identify trends, patterns, and insights to support business decisions
Translate business requirements into analytical approaches, models, and data science solutions
Perform data validation, quality checks, and issue resolution to ensure accuracy and consistency
Collaborate with cross-functional teams including data engineers, analysts, and business stakeholders
Communicate model outputs, analytical findings, and recommendations to both technical and non-technical audiences
Document models, datasets, and methodologies to support reproducibility, transparency, and reuse
Follow data governance, security, and compliance standards within the platform.

Requirements

What you’ll need

Bachelor’s degree in computer science, engineering, mathematics, statistics, or another relevant field
3-8 years of relevant experience in data science, machine learning, or advanced analytics
Strong experience with Python and SQL for data analysis, modeling, and transformation
Experience with Databricks, Spark, Delta Lake, or similar cloud-native data platforms
Hands-on experience designing, building, evaluating, and deploying machine learning models, including experience moving models from prototype to production or production-like environments
Experience with ML lifecycle practices including experiment tracking, model evaluation, model registry, version control, deployment workflows, monitoring, and retraining approaches
Familiarity with model serving patterns, API-based inference, scheduled batch scoring, and integration of model outputs into dashboards, applications, or operational workflows
Experience with data preparation, feature engineering, and model development
Ability to analyze data and communicate insights clearly
Ability to troubleshoot technical issues, communicate recommendations clearly, and work effectively in team-based delivery environments
Experience supporting AI governance practices, including model documentation, validation, monitoring, version control, and responsible AI considerations
Ability to work across data science, data engineering, cloud, security, and client stakeholder teams to translate analytical prototypes into scalable, maintainable solutions.

Benefits

Comp & perks

Competitive compensation
Flexible benefits package
Supportive workplace