Description

Role Overview:

This role focuses on defining, developing, and deploying machine learning solutions, translating business needs into ML approaches, and ensuring models are production-ready and continuously monitored. The ideal candidate will possess strong Python and SQL skills, extensive experience with various ML and Deep Learning frameworks, and practical knowledge of data engineering and cloud platforms.

Key Responsibilities:

• Define ML use cases, success metrics, and evaluation criteria, liaising directly with business stakeholders to translate needs into an ML approach.
• Perform data exploration, quality checks, feature engineering, and dataset preparation for model training and testing.
• Build, train, validate, and iterate ML models, comparing experiments to select the optimal candidate model.
• Package solutions for production, including containerized scoring/service endpoints, and support deployment with engineering/MLOps practices.
• Set up basic monitoring for model accuracy/health and support continuous improvement post-release.

Required Skills:

• Strong Python familiarity for data preparation, modeling, and building ML components.
• Proficiency in SQL, including joins, window functions, CTEs, and query optimization.
• Hands-on experience with Machine Learning algorithms: Linear/Logistic Regression, Decision Trees, Random Forest, XGBoost, LightGBM, SVM, KNN.
• Expertise in model evaluation metrics (Precision/Recall, F1, ROC-AUC, MSE, RMSE) and tuning techniques (Grid search, randomized search, cross-validation).
• Familiarity with Deep Learning frameworks such as TensorFlow, Keras, and PyTorch, applied to CNNs, RNNs, LSTMs, and Transformers for NLP, computer vision, and time-series forecasting.
• Skills in Data Wrangling & Preprocessing: missing data handling, feature engineering, data cleaning, outlier detection, normalization/standardization.
• Experience with Data Visualization & BI Tools: Python libraries (Matplotlib, Seaborn, Plotly) and tools like Tableau, Power BI for dashboards and reporting.
• Knowledge of Big Data Frameworks (Spark, Hadoop) and Cloud Platforms (AWS - S3, EC2, SageMaker; Azure - Data Factory, Databricks, ML Studio; GCP - BigQuery, Vertex AI).
• Deployment Skills: Model deployment using Flask, FastAPI, Docker, Kubernetes (optional), and CI/CD basics.
• Understanding of Databases & Data Engineering Basics: Relational (MySQL, PostgreSQL, SQL Server), NoSQL (MongoDB, Cassandra), and Data pipelines (Airflow, Prefect - optional).

Qualifications:

• Solid foundation in ML concepts (supervised/unsupervised, evaluation, validation) and practical experimentation.
• Experience taking models to production in a cloud-agnostic way, with a portable design and API/service mindset.
• Working knowledge of version control and basic CI/CD-style collaboration with engineering teams

Key Skills

Education

Any Graduate