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.
Role: Data Scientist.
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.
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