Must Have Technical/Functional Skill
Hands-on experience on:
1. Programming Languages
• Strong Python familiarity (hands on) for data prep, modeling, and building ML components.
• SQL - Skills: joins, window functions, CTEs, query optimization
2. Machine Learning
• Linear/Logistic Regression
• Decision Trees, Random Forest, XGBoost, LightGBM
• SVM, KNN
• Model evaluation - Precision/Recall, F1, ROC-AUC, MSE, RMSE
• Model tuning - Grid search, randomized search, cross validation
3. Deep Learning
• Frameworks: TensorFlow, Keras, PyTorch
• CNNs, RNNs, LSTMs, Transformers
• Use cases: NLP, computer vision, time-series forecasting
4. Data Wrangling & Preprocessing
• Missing data handling
• Feature engineering
• Data cleaning
• Outlier detection
• Normalization/standardization
5. Data Visualization & BI Tools
• Python: Matplotlib, Seaborn, Plotly
• 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)
• CI/CD basics
8. Databases & Data Engineering Basics
• Relational: MySQL, PostgreSQL, SQL Server
• NoSQL: MongoDB, Cassandra
• Data pipelines: Airflow, Prefect (optional)
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 f or production (e.g., containerized scoring/service endpoint) and support deployment with engineering/MLOps practices
• Set up basic monitoring (model accuracy/health) and support continuous improvement post release. Required Skills & Experience
• Solid foundation in ML concepts (supervised/unsupervised, evaluation, validation) and practical experimentation.
• Experience taking models to production in a cloud agnostic way (portable design; API/service mindset).
• Working knowledge of version control and basic CI/CD-style collaboration with engineering teams.
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