Job Description :

Required Skills :

- Strong proficiency in Python and the data/ML stack NumPy, Pandas, scikit-learn.

- Strong command of SQL and working with large datasets.

- Hands-on experience building and deploying models across multiple problem types:

1. Classification & regression (e.g. XGBoost, LightGBM, random forests, logistic/linear models)

2. Clustering & segmentation (k-means, hierarchical, density-based)

3. Time-series forecasting

4. Anomaly / outlier detection

- Deep learning architectures CNN and RNN/LSTM applied where the problem warrants (using PyTorch or TensorFlow).

- Solid grounding in statistics, probability, and experiment design (hypothesis testing, A/B testing, causal thinking).

- Experience with the full ML lifecycle data collection, feature engineering, training, evaluation, deployment, monitoring, retraining.

- Practical MLOps experience experiment tracking, model registry, pipelines, CI/CD, production monitoring.

- Strong model evaluation discipline choosing and interpreting the right metrics for each use case.

- Ability to translate model outputs into business impact and communicate to stakeholders.

Good to Have :

- Bayesian modelling experience PyMC (or Stan), especially for MMM and uncertainty-aware prediction.

- Experience with Databricks / Spark for large-scale data processing and ML.

- Familiarity with MLOps tooling MLflow, Weights & Biases, Feature Store, Airflow / Databricks Workflows.

- Experience with a major cloud platform (AWS / GCP / Azure) for model training, serving, and orchestration.

- GenAI / LLM exposure RAG, embeddings, prompt-based or hybrid solutions.

- Recommender systems / personalization at scale.

- Experience deploying real-time / streaming inference.

- Domain exposure to marketing analytics, fintech/fraud, or subscription/SaaS businesses.

Education :

- UG: B.Tech / B.E. in Any Specialization

Key Skills :

- Skills highlighted with are preferred keyskills.

- Data Science, Machine Learning, Deep Learning, Python, LLM.