Data Science Supervisor

Quick Facts

• Role: Data Science Supervisor (hands-on)

• Location: Los Angeles, CA (Hybrid)

• Duration: 12+ months

Description

Lead and mentor a team of data science professionals while providing hands-on technical leadership across data science, data engineering, ETL pipelines, and cloud environments. Oversee data quality and security standards, guide machine learning and statistical analysis, and ensure solutions meet business and operational requirements. Collaborate with engineers and stakeholders to translate business needs into scalable technical solutions and drive Agile/Scrum deliverables.

Responsibilities

• Supervise and mentor data science professionals; coordinate complex data science projects

• Provide technical leadership for databases, ETL, backend pipelines, and cloud environments (including Databricks)

• Oversee data engineering, pipeline design, data warehousing, and database architecture

• Lead technical/code-level documentation, including API layouts and ETL business logic documentation

• Establish standards for data security implementation, QA, unit testing, and code integrity

• Guide supervised and unsupervised machine learning model development

• Oversee statistical analysis including hypothesis testing, regression, classification, clustering, and dimensionality reduction

• Support ML deployment using Docker, Flask, and cloud platforms such as AWS and Azure

• Review technical deliverables to ensure alignment with business and operational needs

• Participate in Agile/Scrum activities (sprint planning, daily stand-ups, feasibility assessments, blockers)

• Coordinate deployment and change management across project teams

• Translate stakeholder/business requirements into scalable technical solutions

Requirements

• 6+ years of relevant professional experience

• At least 3 years supervising a team of data science professionals

• Strong hands-on experience with Python, R, and SQL

• Proficiency with ML techniques (supervised and unsupervised)

• Strong statistical analysis and hypothesis testing experience

• Experience with big data technologies (Apache Spark, Hadoop)

• Hands-on ETL, backend data pipelines, relational databases, and data warehousing experience

• Experience with AWS, Azure, and Databricks

• Experience deploying ML models using Docker and Flask or cloud-based platforms

• Advanced data visualization skills (Tableau, Power BI, Matplotlib, Seaborn, etc.)

• Knowledge of API design, system documentation, data security, and QA practices

• Experience with Agile/Scrum methodologies and technical project coordination

• Master’s degree in a related field (or qualifying master’s degree substitution for up to 2 years of required experience)