Data Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop, train, and assess machine learning and statistical models utilizing the Databricks platform.

• Prepare, cleanse, and manage datasets for modeling, experimentation, and analysis.

• Write, optimize, and sustain Python and SQL workflows for data exploration, feature engineering, and model development.

• Handle large-scale datasets employing Databricks, Spark, and Delta Lake.

• Create reusable feature engineering workflows and model training pipelines using Databricks notebooks, workflows, and MLflow.

• Register, version, promote, and document models in accordance with MLflow Model Registry and Unity Catalog governance practices.

• Monitor the performance, drift, data quality, usage patterns, and operational issues of deployed models.

• Suggest actions regarding model retraining, tuning, or retirement as necessary.

• Analyze data to uncover trends, patterns, and insights that support business decisions.

• Convert business requirements into analytical strategies, models, and data science solutions.

• Conduct data validation, quality checks, and resolve issues.

• Collaborate with data engineers, analysts, and business stakeholders.

• Communicate model outputs, analytical results, and recommendations to both technical and non-technical audiences.

• Document models, datasets, and methodologies to ensure reproducibility, transparency, and reuse.

• Adhere to data governance, security, and compliance standards.

⛳️ Requirements

• Bachelor’s degree in computer science, engineering, mathematics, statistics, or a related field.

• 3–8 years of applicable experience in data science, machine learning, or advanced analytics.

• Proficient in Python and SQL for data analysis, modeling, and transformation.

• Experience with Databricks, Spark, Delta Lake, or comparable cloud-native data platforms.

• Practical experience in designing, building, assessing, and deploying machine learning models.

• Familiarity with transitioning models from prototype to production or production-like environments.

• Knowledge of ML lifecycle practices including experiment tracking, model evaluation, model registry, version control, deployment workflows, monitoring, and retraining methods.

• Understanding of model serving patterns, API-based inference, scheduled batch scoring, and integrating model outputs into dashboards, applications, or operational workflows.

• Experience in data preparation, feature engineering, and model development.

• Capable of analyzing data and conveying insights clearly.

• Proficient in troubleshooting technical issues and effectively collaborating in team-oriented delivery environments.

• Experience supporting AI governance practices, including model documentation, validation, monitoring, version control, and responsible AI considerations.

• Ability to collaborate across data science, data engineering, cloud, security, and client stakeholder teams.

• Nice to have: 2+ years of hands-on experience with Databricks.

• Nice to have: an active Databricks Machine Learning Engineer, GenAI Engineer, Data Analyst, or related certification.

• Nice to have: experience with Databricks MLflow, Feature Engineering, Feature Store, Model Serving, Workflows, Unity Catalog, Mosaic AI, Vector Search, AI Gateway, or similar capabilities.

• 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.

• Nice to have: familiarity with machine learning frameworks and statistical modeling techniques.

• Nice to have: experience with Azure, AWS, or GCP.

• Nice to have: experience in project-based or consulting delivery environments.

• Nice to have: familiarity with data modeling, data warehousing, and large-scale data processing concepts.

🏝️ Benefits

• Comprehensive, total rewards package.

• Competitive compensation.

• Flexible benefits package.

• Diverse and supportive workplace.

• Reasonable accommodation support.