RandomTrees
Data Scientist
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist with a contract length of "unknown" and a pay rate of "$X/hour." Key skills include Python, SQL, time-series modeling, and experience with cloud platforms. Industry experience in marketing analytics is required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
December 18, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
San Francisco Bay Area
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🧠 - Skills detailed
#Snowflake #GCP (Google Cloud Platform) #ML (Machine Learning) #Monitoring #CRM (Customer Relationship Management) #Spark (Apache Spark) #PySpark #NumPy #Azure #Batch #Python #A/B Testing #Statistics #AWS (Amazon Web Services) #Redshift #SQL (Structured Query Language) #Data Science #Pandas #Anomaly Detection #Cloud #Visualization #BigQuery #Airflow #Data Engineering
Role description
Key Responsibilities
• Design and productionize models for opportunity scanning, anomaly detection, and significant change detection across CRM, streaming, ecommerce, and social data.
• Define and tune alerting logic (thresholds, SLOs, precision/recall) to minimize noise while surfacing high-value marketing actions.
• Partner with marketing, product, and data engineering to operationalize insights into campaigns, playbooks, and automated workflows, with clear monitoring and experimentation.
Required Qualifications
• Strong proficiency in Python (pandas, NumPy, scikit-learn; plus experience with PySpark or similar for large-scale data) and SQL on modern warehouses (e.g., BigQuery, Snowflake, Redshift).
• Hands-on experience with time-series modeling and anomaly / changepoint / significant-movement detection(e.g., STL decomposition, EWMA/CUSUM, Bayesian/prophet-style models, isolation forests, robust statistics).
• Experience building and deploying production ML pipelines (batch and/or streaming), including feature engineering, model training, CI/CD, and monitoring for performance and data drift.
• Solid background in statistics and experimentation: hypothesis testing, power analysis, A/B testing frameworks, uplift/propensity modeling, and basic causal inference techniques.
• Familiarity with cloud platforms (GCP/AWS/Azure), orchestration tools (e.g., Airflow/Prefect), and dashboarding/visualization tools to expose alerts and model outputs to business users.
Key Responsibilities
• Design and productionize models for opportunity scanning, anomaly detection, and significant change detection across CRM, streaming, ecommerce, and social data.
• Define and tune alerting logic (thresholds, SLOs, precision/recall) to minimize noise while surfacing high-value marketing actions.
• Partner with marketing, product, and data engineering to operationalize insights into campaigns, playbooks, and automated workflows, with clear monitoring and experimentation.
Required Qualifications
• Strong proficiency in Python (pandas, NumPy, scikit-learn; plus experience with PySpark or similar for large-scale data) and SQL on modern warehouses (e.g., BigQuery, Snowflake, Redshift).
• Hands-on experience with time-series modeling and anomaly / changepoint / significant-movement detection(e.g., STL decomposition, EWMA/CUSUM, Bayesian/prophet-style models, isolation forests, robust statistics).
• Experience building and deploying production ML pipelines (batch and/or streaming), including feature engineering, model training, CI/CD, and monitoring for performance and data drift.
• Solid background in statistics and experimentation: hypothesis testing, power analysis, A/B testing frameworks, uplift/propensity modeling, and basic causal inference techniques.
• Familiarity with cloud platforms (GCP/AWS/Azure), orchestration tools (e.g., Airflow/Prefect), and dashboarding/visualization tools to expose alerts and model outputs to business users.
