Data Scientist with Machine Learning Engineer

Key Responsibilities
Data Science & Analytics

• Explore, clean, and analyze structured and unstructured datasets.
• Apply statistical techniques to generate insights and support business decisions.
• Develop predictive models using regression, classification, clustering, NLP, or time-series methods.
• Conduct hypothesis testing, A/B experiments, and exploratory data analysis (EDA).

Machine Learning Engineering

• Build, train, and fine-tune ML models using frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Develop and maintain end-to-end ML pipelines, including feature engineering, model training, evaluation, and versioning.
• Deploy ML models into production using MLOps pipelines and tools like MLflow, Kubeflow, SageMaker, or Vertex AI.
• Implement monitoring, retraining strategies, and improve model performance.
• Optimize models for scalability, latency, and resource efficiency.
• Requirements

Must-Have

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, AI/ML, or related field.
• 3–7+ years of experience as a Data Scientist, ML Engineer, or similar role.
• Strong programming skills in Python (NumPy, Pandas, Scikit-learn, Matplotlib, etc.).
• Experience with ML frameworks (TensorFlow, PyTorch).
• Strong understanding of machine learning algorithms, statistics, and data modeling.
• Experience deploying models in production (Docker, APIs, cloud deployment).
• Knowledge of SQL and experience with relational and NoSQL databases.
• Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, DVC)