Note: The job is a remote job and is open to candidates in USA. Guidehouse is a consulting company seeking a Databricks Data Scientist to join its AI & Data team. The role supports client projects by developing and deploying machine learning and statistical models, preparing and analyzing large-scale datasets, building Databricks-based workflows, and communicating data-driven insights to technical and non-technical stakeholders.
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
Skills
- 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
- 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
Benefits
- Virtual work flexibility within the United States
- A comprehensive, total rewards package
- A flexible benefits package
Company Overview
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