Data Scientist – Machine Learning

Key Responsibilities:

1. Machine Learning Development

• Design, build, and evaluate supervised and unsupervised ML models (e.g., regression, classification, clustering, recommendation systems).
• Perform feature engineering, model tuning, and validation using cross-validation and performance metrics.

2. Data Preparation & Analysis

• Clean, preprocess, and transform large datasets from various sources.
• Conduct exploratory data analysis (EDA) to uncover patterns and insights.

3. Model Deployment & Monitoring

• Deploy ML models into production using tools like Flask, FastAPI, or cloud-native services.
• Monitor model performance and retrain/update models as needed.

4. Collaboration & Communication

• Work closely with data engineers, product managers, and business stakeholders to understand requirements and deliver impactful solutions.
• Present findings and model outcomes in a clear, actionable manner.

5. Tools & Technologies

• Use Python and libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Leverage version control (Git), Jupyter notebooks, and ML lifecycle tools (MLflow, DVC).

Preferred Qualifications:

• Bachelor’s or master’s degree in computer science, Data Science, Statistics, or a related field.
• 2–3 years of experience in building and deploying ML models.
• Strong programming skills in Python; familiarity with SQL.
• Solid understanding of ML concepts, model evaluation, and statistical techniques.
• Exposure to cloud platforms (AWS, GCP, or Azure) and MLOps practices is a plus.
• Excellent problem-solving and communication skills.