Description

Design, build, and deploy machine learning models for production environments, translating business requirements into scalable data solutions.

This role is on-site.

Responsibilities

• Define ML use cases and success metrics, liaising directly with business stakeholders to translate needs into technical approaches.
• Perform data exploration, quality checks, feature engineering, and dataset preparation for training and testing.
• Build, train, validate, and iterate on ML models, comparing experiments to select optimal candidates.
• Package solutions for production using containerized scoring or service endpoints, supporting deployment with MLOps practices.
• Set up basic model monitoring for accuracy and health, supporting continuous improvement post-release.

Required Skills

• Strong hands-on Python proficiency for data preparation, modeling, and ML component development.
• Advanced SQL skills including joins, window functions, CTEs, and query optimization.
• Experience with ML libraries and algorithms: Scikit-learn (Linear/Logistic Regression, Decision Trees, Random Forest, XGBoost, LightGBM, SVM, KNN) and evaluation metrics (Precision/Recall, F1, ROC-AUC, MSE, RMSE).
• Deep learning framework experience with TensorFlow, Keras, and PyTorch (CNNs, RNNs, LSTMs, Transformers).
• Big data processing experience with Spark and Hadoop.
• Cloud platform experience in at least one major provider: AWS (S3, EC2, SageMaker), Azure (Data Factory, Databricks, ML Studio), or GCP (BigQuery, Vertex AI).
• Data visualization and BI tool proficiency with Tableau, Power BI, Matplotlib, Seaborn, or Plotly.
• Ability to take models to production in a cloud-agnostic manner with API/service mindset.
• Working knowledge of version control and basic CI/CD-style collaboration.

Preferred Skills

• Experience with model deployment tools such as Flask, FastAPI, Docker, or Kubernetes.
• Familiarity with data pipeline tools like Airflow or Prefect.

Key Skills

Education

Any Graduate