Databricks Data Scientist

Guidehouse•Tysons, VA

19h•$113,000 - $188,000•Remote

About The Position

Guidehouse is seeking a Databricks Data Scientist to join its AI & Data team and support client projects involving advanced analytics, machine learning, and data science solutions. The role uses data to develop models, generate insights, and support data-driven decision-making. It requires strong Python and SQL skills, analytical thinking, and collaboration with clients and stakeholders to deliver scalable data science solutions. The position offers virtual work flexibility within the United States. Remote candidates will be considered, with preference for candidates near Guidehouse offices in Arlington, VA; Washington, DC; New York, NY; Chicago, IL; Austin, TX; Atlanta, GA; Boston, MA; or Boulder, CO. Hiring is across multiple levels for the Databricks Data Science Teams. The posted salary range varies based on experience, qualifications, location, and the level at which the position is filled. The annual salary range is $113,000–$188,000. Compensation decisions may depend on skill sets, experience and training, security clearances, licensure and certifications, and other business and organizational needs.

Requirements

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 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 integrating 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.

Nice To Haves

2+ years of hands-on experience with the Databricks platform.
Active Databricks Machine Learning Engineer, GenAI Engineer, Data Analyst, or related certification.
Experience with Databricks MLflow, Feature Engineering, Feature Store, Model Serving, Workflows, Unity Catalog, Mosaic AI, Vector Search, AI Gateway, or related Databricks AI/ML capabilities.
Experience developing GenAI, LLM, RAG, agentic AI, or prompt evaluation workflows using Databricks Mosaic AI, MLflow, open-source frameworks, or cloud-native AI services.
Experience with CI/CD, automated testing, code packaging, environment promotion, and source control practices for data science and machine learning workloads.
Experience with machine learning frameworks and statistical modeling techniques.
Experience with cloud platforms such as Azure, AWS, or GCP.
Experience working in project-based or consulting delivery environments.
Familiarity with data modeling, data warehousing, and large-scale data processing concepts.

Responsibilities

Develop, train, and evaluate machine learning and statistical models on Databricks to support business and mission needs.
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.
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 as needed.
Analyze data to identify trends, patterns, and insights that 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 data engineers, analysts, business stakeholders, and other cross-functional teams.
Communicate model outputs, analytical findings, and recommendations to 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.