Databricks Data Scientist

Guidehouse•Tysons, VA

8d•$113,000 - $188,000•Remote

About The Position

Guidehouse is seeking a Databricks Data Scientist to join our AI & Data team to support client projects involving advanced analytics, machine learning, and data science solutions. This role focuses on working with data to develop models, generate insights, and support data-driven decision-making across teams. The role requires strong Python and SQL skills, analytical thinking, and the ability to collaborate with clients and stakeholders to deliver scalable data science solutions. This position offers virtual work flexibility within the United States. While remote candidates will be considered, preference will be given to candidates located near a Guidehouse office in one of the following markets: Arlington, VA; Washington, DC; New York, NY; Chicago, IL; Austin, TX; Atlanta, GA; Boston, MA; and Boulder, CO. Please note, this requisition supports hiring across multiple levels to support our Databricks Data Science Teams. The posted salary range represents a range of potential compensation and will vary based on the selected candidate’s experience, qualifications, location, and the level at which the position is filled.

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 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.

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 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.