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
LinearLogistic Regression
Decision Trees, Random Forest, XGBoost, LightGBM
SVM, KNN
Model evaluation - PrecisionRecall, 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
Normalizationstandardization 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) CICD 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 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.
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