NextGenPros Inc

Data Scientist

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist in San Francisco, CA (Hybrid) on a contract basis, requiring 13-14+ years of experience. Key skills include Python, SQL, time-series modeling, ML pipelines, and cloud platforms.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
Unknown
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πŸ—“οΈ - Date
March 20, 2026
πŸ•’ - Duration
Unknown
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🏝️ - Location
Hybrid
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πŸ“„ - Contract
Unknown
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πŸ”’ - Security
Unknown
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πŸ“ - Location detailed
San Francisco Bay Area
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🧠 - Skills detailed
#Pandas #Statistics #Visualization #Anomaly Detection #Snowflake #BigQuery #A/B Testing #AWS (Amazon Web Services) #PySpark #Spark (Apache Spark) #Batch #NumPy #Cloud #ML (Machine Learning) #SQL (Structured Query Language) #Azure #Data Science #Data Engineering #Redshift #GCP (Google Cloud Platform) #Python #CRM (Customer Relationship Management) #Monitoring #Airflow
Role description
Position : Data Scientist Location : San Francisco , CA-Hybrid Type of Hire : Contract Need 13-14 + years of Experience candidates Key Responsibilities β€’ Design and productionize models for opportunity scanning, anomaly detection, and significant change detection across CRM, streaming, ecommerce, and social data. β€’ Define and tune alerting logic (thresholds, SLOs, precision/recall) to minimize noise while surfacing high-value marketing actions. β€’ Partner with marketing, product, and data engineering to operationalize insights into campaigns, playbooks, and automated workflows, with clear monitoring and experimentation. Required Qualifications β€’ 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). β€’ 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). β€’ 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. β€’ Solid background in statistics and experimentation: hypothesis testing, power analysis, A/B testing frameworks, uplift/propensity modeling, and basic causal inference techniques. β€’ 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.