Data Scientist -
Location: Glendale, CA (Hybrid, 2 4 days/week onsite)
Contract
Must-Haves: Strong ML deployment/productionization background, Python, Snowflake (or cloud DB equivalent), MLOps pipelines, and strong data storytelling for leadership.
Position Overview
As a Senior Data Scientist, you will sit at the intersection of machine learning, data engineering,
and strategic decision-making. You will design, operationalize, and maintain high-impact
production ML pipelines, predictive models, and analytical tools supporting both financial and
operational use cases. Working collaboratively across cross-functional engineering and
leadership teams, you will transform massive datasets into actionable strategic intelligence.
Key Responsibilities & Day-to-Day Impact
Machine Learning Development: Design, build, and deploy novel AI/ML solutions,
predictive models, and statistical algorithms to support financial forecasting and studio
operations.
Productionization & MLOps: Architect efficient data models and robust production
pipelines; partner with Data Engineering and Infrastructure teams to ensure repeatable,
maintainable, and scalable MLOps environments (leveraging Snowflake).
End-to-End Lifecycle Execution: Own the full data science lifecycle including data
collection, feature engineering, selection, model training, evaluation, deployment, and
ongoing performance monitoring.
Data Storytelling & Insights: Extract trends and insights from large datasets;
communicate findings via effective data visualizations to influence business leaders and
executive stakeholders.
Technical Leadership: Provide technical guidance and mentorship to junior engineers
while researching emerging AI/ML technologies to keep the organization at the cutting
edge.
Candidate Qualifications
Top Must-Have Requirements:
1. Proven ML Expertise: Demonstrated track record of designing, building, and deploying
machine learning models to solve complex operational or financial problems.
2. Core Programming Languages: Expert proficiency in Python (along with libraries such
as scikit-learn, NumPy, and Pandas), R, or Scala.
3. Data Warehouse Platforms: Hands-on experience with Snowflake (or cloud databases
such as Databricks and BigQuery).
4. MLOps & Pipeline Productionization: Direct experience operationalizing ML pipelines
end to end, ensuring repeatability, scalability, and monitoring in production.
5. Data Storytelling: Strong communication and presentation skills to convey technical ML
concepts effectively to executive leadership and key stakeholders.
Education & Preferred Experience:
Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a
related quantitative field (Master's degree preferred).
5+ years of dedicated experience delivering successful data science and machine
learning projects.
Experience with financial forecasting models.
Familiarity with data orchestration and UI tools (e.g., dbt, Airflow, Streamlit).
Interview Process
1. Round 1: Technical Interview - Deep dive into machine learning fundamentals,
coding proficiency, and problem-solving abilities.
2. Round 2: Team & Behavioral Assessment - Focus on cultural alignment,
cross-functional collaboration, and team dynamics.
3. Round 3: Final Leadership Discussion - Final strategic conversation with hiring
manager leadership
