Data Scientist Traditional ML

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 developmentfrom 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: Bachelors degree in Computer Science, Statistics, Applied Mathematics, or a related field; Masters preferred. 610 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 theyve led. #J-18808-Ljbffr