Data Scientist

Hi,

Hope you are doing well.

Please find the below job description and let me know if you have any suitable profiles.

Role: Data Scientist

Location: Los Angeles, CA (5 Days working onsite from client office) – Need only Locals.

Looking for a strong Data Scientist with hands-on experience in Machine Learning, Python, Snowflake and MLOps, someone who can build models and successfully take them from experimentation to production.

You’ll work across the ML lifecycle, from data and feature engineering to predictive modeling, deployment, monitoring and scalable production pipelines, while solving real financial and operational business challenges.

Experience with Databricks, BigQuery, dbt, Airflow, Streamlit or financial forecasting is an added advantage.

Location: LA, California (working onsite from client office)

Interested or know someone who fits? Please reach out or share!

 Key requirements:

• 5+ years of Data Science experience
• Strong ML model development experience and excellent understanding of ML technologies, models, mathematics/statistical methods
• Python: scikit-learn, NumPy, pandas
• Snowflake
• MLOps and production deployment of ML models
• End-to-end experience from feature engineering/model development through deployment and monitoring

Key responsibilities:

• Design, build, and deploy predictive models, ML algorithms, and statistical analyses to address complex business challenges.
• Identify trends, patterns, and insights from large datasets to inform decision-making.
• Architect efficient data models and high-quality production pipelines.

Deployment of ML models:

• - End-to-end experience from feature engineering/model development through deployment and monitoring.

Key Responsibilities:

• Design, build, and deploy predictive models, ML algorithms, and statistical analyses to address complex business challenges.
• Identify trends, patterns, and insights from large datasets to inform decision-making.
• Architect efficient data models and high-quality production pipelines.
• Work end to end from data collection, feature generation and selection, algorithm development, forecasting, visualization, and communicating model results.
• Monitor and maintain the performance of ML models.
• Collaborate with data engineering teams to ensure the quality and accessibility of data.
• Mentor as needed.