Job Family:
Data Science & Analysis, Data Science Consulting
Travel Required:
Clearance Required:
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.
What You Will Do:
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.
What You Will 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.
What Would Be Nice to Have:
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.
What We Offer:
Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace.
About Guidehouse
Guidehouse is an Equal Opportunity Employer–Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation.
Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco.
If you have visited our website for information about employment opportunities, or to apply for a position, and you require an accommodation, please contact Guidehouse Recruiting at 1-571-633-1711 or via email at [email protected]. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodation.
All communication regarding recruitment for a Guidehouse position will be sent from Guidehouse email domains including @guidehouse.com or [email protected]. Correspondence received by an applicant from any other domain should be considered unauthorized and will not be honored by Guidehouse. Note that Guidehouse will never charge a fee or require a money transfer at any stage of the recruitment process and does not collect fees from educational institutions for participation in a recruitment event. Never provide your banking information to a third party purporting to need that information to proceed in the hiring process.
If any person or organization demands money related to a job opportunity with Guidehouse, please report the matter to Guidehouse’s Ethics Hotline. If you want to check the validity of correspondence you have received, please contact [email protected]. Guidehouse is not responsible for losses incurred (monetary or otherwise) from an applicant’s dealings with unauthorized third parties.
Guidehouse does not accept unsolicited resumes through or from search firms or staffing agencies. All unsolicited resumes will be considered the property of Guidehouse and Guidehouse will not be obligated to pay a placement fee.
Skills Required
- Bachelor's degree in computer science, engineering, mathematics, statistics, or relevant field.
- 3-8 years of relevant experience in data science, machine learning, or advanced analytics.
- Strong experience with Python for data analysis, modeling, and transformation.
- Strong experience with SQL for data analysis, modeling, and transformation.
- Experience with Databricks platform.
- Experience with Apache Spark and Delta Lake or similar cloud-native data platforms.
- Hands-on experience designing, building, evaluating, and deploying machine learning models into production or production-like environments.
- Experience with ML lifecycle practices including experiment tracking, model evaluation, model registry, version control, deployment workflows, monitoring, and retraining.
- Experience with MLflow and MLflow Model Registry.
- Familiarity with model serving patterns, API-based inference, and scheduled batch scoring integration.
- Experience with data preparation, feature engineering, and model development.
- Ability to analyze data and communicate insights clearly to technical and non-technical audiences.
- Ability to troubleshoot technical issues and work effectively in team-based delivery environments.
- Experience supporting AI governance practices, including model documentation, validation, monitoring, and responsible AI considerations.
- Ability to work across data science, data engineering, cloud, security, and stakeholder teams to translate prototypes into scalable solutions.
- 2+ years hands-on experience with Databricks platform (listed as nice-to-have).
- Active Databricks Machine Learning Engineer, GenAI Engineer, Data Analyst, or related certification (nice-to-have).
- Experience with Databricks MLflow, Feature Store, Model Serving, Unity Catalog, Mosaic AI, Vector Search, AI Gateway (nice-to-have).
- Experience developing GenAI, LLM, RAG, agentic AI, or prompt evaluation workflows (nice-to-have).
- Experience with CI/CD, automated testing, code packaging, environment promotion, and source control practices (nice-to-have).
- Experience with cloud platforms such as Azure, AWS, or GCP (nice-to-have).
- Experience working in project-based or consulting delivery environments (nice-to-have).
- Familiarity with data modeling, data warehousing, and large-scale data processing concepts (nice-to-have).
Guidehouse Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Guidehouse and has not been reviewed or approved by Guidehouse.
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Healthcare Strength — Feedback suggests the benefits package includes broad medical, dental, vision, prescription, life, and disability coverage, which is often seen as a strong foundational offering. Access to HSA/FSA options further supports day-to-day healthcare and dependent-care needs.
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Parental & Family Support — Feedback suggests parental leave and adoption assistance are available, alongside an emergency back-up childcare program. These offerings indicate meaningful support for employees managing family responsibilities.
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Wellbeing & Lifestyle Benefits — Feedback suggests flexible work options and counseling/EAP-style support are part of the broader rewards mix. Additional lifestyle-oriented perks like community events and referral programs are also described as available in some contexts.
Guidehouse Insights
What We Do
Guidehouse is a leading global provider of consulting services to the public sector and commercial markets, with broad capabilities in management, technology, and risk consulting. By combining our public and private sector expertise, we help clients address their most complex challenges and navigate significant regulatory pressures focusing on transformational change, business resiliency, and technology-driven innovation. Across a range of advisory, consulting, outsourcing, and digital services, we create scalable, innovative solutions that help our clients outwit complexity and position them for future growth and success. The company has more than 12,000 professionals in over 50 locations globally. Guidehouse is a Veritas Capital portfolio company, led by seasoned professionals with proven and diverse expertise in traditional and emerging technologies, markets, and agenda-setting issues driving national and global economies.








