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 GCP (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 for 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.
· 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.