Requirements & Qualifications:
Education
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related technical field.
Technical Experience
- 5+Years years of hands-on experience developing, training, and deploying machine learning models in production environments.
- Proficiency in Python and major ML frameworks such as Scikit-learn, TensorFlow, PyTorch, or Keras.
- Strong experience with data manipulation and analysis using Pandas and NumPy.
- Skilled in data visualization using libraries such as Matplotlib and Seaborn.
- Solid understanding of supervised, unsupervised, and deep learning techniques.
- Experience performing end-to-end Data Analytics, including data cleaning, trend analysis, reporting, and insight generation.
Core Data Skills
- Strong capability in Exploratory Data Analysis (EDA).
- Experience with feature engineering and data preprocessing.
- Good grounding in statistics and mathematical concepts relevant to machine learning.
MLOps & Deployment
- Familiarity with MLOps tools such as MLflow, Kubeflow, or similar platforms.
- Experience working with cloud services (AWS, GCP, or Azure) for model development and deployment.
- Proficiency with version control systems (Git) and CI/CD practices for ML pipelines.
Preferred Qualifications:
- Exposure to NLP, Computer Vision, or Time-Series modeling.
- Experience working with large-scale distributed systems or Big Data technologies like Spark or Hadoop.
- Knowledge of data warehousing solutions and SQL.
