Location:  Atlanta (Hybrid)
Type: Contract
Experience Required: 6 + Years

Position Overview:

We are seeking an experienced Data Scientist with deep expertise in traditional machine learning approaches to join our analytics team. The ideal candidate will have strong hands-on experience across the full lifecycle of predictive model development—from data preprocessing and exploratory data analysis (EDA) to model building and evaluation.

Key Responsibilities:

• Lead the end-to-end ML model lifecycle: data collection, preprocessing, exploratory data analysis (EDA), feature engineering, model development, evaluation, and deployment.
• Develop and optimize classification models (Logistic Regression, Decision Tree, Random Forest) for targeted business use cases. 
• Build high-performing regression models (Linear Regression, Gradient Boosting, Neural Networks, K-Nearest Neighbors) to support quantitative decision-making. 
• Apply unsupervised learning methods (K-means and other clustering approaches) to identify patterns, segment data, and detect anomalies. 
• Collaborate with stakeholders to translate business challenges into data science initiatives.
• Evaluate model performance using appropriate metrics and iterate rapidly. 
• Communicate findings and insights clearly to technical and non-technical audiences. 
• Document methodologies and maintain best practices to ensure reproducibility and knowledge sharing.

Must – Have Qualifications:

• Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, or a related field; Master’s preferred.
• 6–10 years of hands-on experience in machine learning with focus on predictive modeling.
• Proven expertise in building and deploying:
• Classification: Logistic Regression, Decision Trees, Random Forests
• Regression: Linear Regression, Gradient Boosting, Neural Networks, KNN
• Unsupervised Learning: K-means and other clustering techniques.
• Proficiency in Python and libraries like NumPy, pandas, scikit-learn, with familiarity in TensorFlow or PyTorch.
• Strong foundation in statistical modeling, hypothesis testing, and performance metrics.
• Experience with SQL and big data frameworks (e.g., Spark) is a plus.
• Excellent communication skills and ability to translate technical results into business impact.
• Familiarity with data visualization tools (Tableau, Power BI) is preferred.

Preferred Qualifications:

• Prior experience in insurance or financial services domains. 
• Exposure to cloud ML platforms like AWS SageMaker, Azure ML, or Google AI Platform.
• Awareness of MLOps best practices, including monitoring, version control, and deployment pipelines.

Application Process: Qualified candidates should submit their resume, cover letter, and a brief description of relevant projects they’ve led.