Hiring For ML Engineer - Data Scientist

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:
• Classification & regression (e.g. XGBoost, LightGBM, random forests, logistic/linear models)
• Clustering & segmentation (k-means, hierarchical, density-based)
• Time-series forecasting
• 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