Job Title: Data Scientist
Location: Portland, OR 
Duration: 12 Months + Extension
Bill Rate: $80/hour (C2C)
Job Type: W-2, 1099, C2C
Client: To Be Discussed Later
Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, Other

Job Summary:

The Data Scientist will analyze large and complex datasets to identify patterns, develop predictive models, and deliver data-driven business solutions. This role requires a combination of statistical expertise, programming skills, and machine learning knowledge to solve real-world business problems. The candidate will work closely with data engineers, analysts, and business stakeholders to transform raw data into actionable insights. Experience with Python, SQL, predictive analytics, and cloud-based data platforms is essential.

Statistical Analysis & Predictive Modeling: 

• Perform exploratory data analysis (EDA) to identify trends, correlations, anomalies, and patterns within structured and unstructured datasets.
• Develop predictive and statistical models using regression, classification, clustering, and forecasting techniques.
• Apply supervised and unsupervised machine learning algorithms to solve business problems.
• Perform feature engineering, model training, validation, and hyperparameter tuning.
• Conduct hypothesis testing, A/B testing, and statistical experiments to evaluate model performance.
• Measure model accuracy using appropriate evaluation metrics and continuously improve model effectiveness.

Advanced Modeling & Research:

• Apply supervised, unsupervised, and reinforcement learning techniques to large-scale datasets.
• Develop deep learning solutions using PyTorch, TensorFlow, and Scikit-learn.
• Perform feature engineering, statistical analysis, hypothesis testing, and model validation.
• Work with NLP, computer vision, time-series forecasting, and advanced analytics.
• Evaluate emerging algorithms and research methodologies to improve model accuracy and scalability.
• Design A/B testing and experimentation frameworks to measure model effectiveness.

Production AI & Data Science Platforms:

• Build scalable data processing pipelines using Python, SQL, Apache Spark, and PySpark.
• Deploy and manage machine learning models using MLflow, Databricks, or cloud-based ML platforms.
• Implement MLOps practices covering model versioning, CI/CD, monitoring, and lifecycle management.
• Work with AWS, Azure, or Google Cloud for model training and deployment.
• Explore LLMs, Generative AI, RAG architectures, and fine-tuning techniques for enterprise use cases.
• Ensure model explainability, data quality, governance, and responsible AI practices.

Technical Leadership & Qualifications:

• 10+ years of experience in Data Science, Machine Learning, Advanced Analytics, or AI engineering.
• Strong proficiency in Python, SQL, statistical programming, and machine learning algorithms.
• Advanced experience with PyTorch, TensorFlow, Scikit-learn, and distributed data processing.
• Proven ability to take ML models from research and experimentation through production deployment.
• Experience with MLOps, cloud platforms, model monitoring, and enterprise data environments.
• Strong understanding of statistics, probability, optimization, and mathematical modeling.
• Master's or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative discipline preferred.
• Excellent technical leadership, communication, mentoring, and stakeholder management skills.

Preferred Expertise:

• Experience with Generative AI, LLM fine-tuning, NLP, and Transformer architectures.
• Knowledge of Apache Spark, Databricks, MLflow, and large-scale feature engineering.
• Research publications, patents, conference presentations, or open-source contributions are a plus.