Roles & Responsibilities
Define the ML use case, success metrics, and evaluation criteria Liaise with business directly and translate business needs into an ML approach.
Perform data exploration, data quality checks, feature engineering, and dataset preparation for training and testing.
Build, train, validate, and iterate ML models compare experiments and select the best candidate model.
Package the solution for production (e.g., containerized scoringservice endpoint) and support deployment with engineeringMLOps practices
Set up basic monitoring (model accuracyhealth) and support continuous improvement
post-release. Required Skills & Experience
Solid foundation in ML concepts (supervisedunsupervised, evaluation, validation) and practical experimentation.
Experience taking models to production in a cloud-agnostic way (portable design APIservice mindset).
Working knowledge of version control and basic CICD-style collaboration with engineering teams.
Tools: Tableau, Power BI Dashboards, reporting, storytelling with data 6. Big Data & Cloud Tools (Needed for production-scale roles) Big Data Frameworks: Spark, Hadoop Cloud Platforms (any one strongly): o AWS (S3, EC2, SageMaker) o Azure (Data Factory, Databricks, ML Studio) o Google Cloud Platform (BigQuery, Vertex AI) 7. Deployment Skills (advanced roles) Model deployment: Flask, FastAPI Docker, Kubernetes (optional) CICD basics 8. Databases & Data Engineering Basics Relational: MySQL, PostgreSQL, SQL Server NoSQL: MongoDB, Cassandra Data pipelines: Airflow
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