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Location: Charlotte,NC
Exp: 3-6 Years
We are looking for a Machine Learning Engineer / Data Scientist with 3–6 years of hands-on experience to join our growing data science team. You will be working on the design, development, and deployment of scalable machine learning models that drive intelligent decision-making across business units. This is an excellent opportunity to contribute to real-world ML products and shape innovative solutions in a data-driven environment.
Design and implement machine learning models to address business problems including classification, regression, and clustering.
Collaborate with cross-functional teams including data engineers, software developers, and domain experts to build and integrate ML solutions.
Perform data preprocessing, exploration, and feature engineering using tools like Pandas and NumPy .
Develop and evaluate models using libraries such as Scikit-learn , XGBoost , TensorFlow , or PyTorch .
Work with large-scale datasets to build models that are robust, interpretable, and production-ready.
Monitor model performance and assist in retraining and tuning when necessary.
Document model assumptions, development steps, and performance metrics for internal governance and review.
Bachelor’s or Master’s degree in Computer Science , Data Science , AI , Engineering , or related fields.
3–6 years of experience in machine learning or data science roles.
Proficient in Python , with strong knowledge of ML libraries like Scikit-learn, XGBoost, TensorFlow, or PyTorch .
Comfortable writing and optimizing SQL queries.
Hands-on experience with data wrangling and manipulation using Pandas or similar tools.
Solid understanding of key machine learning concepts: classification, regression, clustering, cross-validation, evaluation metrics, feature selection , and hyperparameter tuning .
Exposure to cloud platforms such as AWS , Azure , or Google Cloud Platform (GCP) .
Experience using ML tools like SageMaker , Databricks , or Vertex AI .
Familiarity with MLOps concepts and tools such as MLflow , Airflow , or Kubeflow .
Domain knowledge or prior experience in financial services , especially building models for credit scoring , risk assessment , or fraud detection .
Understanding of regulatory requirements , model documentation standards , and governance protocols in the financial sector.
Exposure to model monitoring frameworks and alerting pipelines.
Experience with notebooks (Jupyter, Colab) and version control (Git).
Background in statistics or a quantitative field is a strong plus.
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