Senior AI Data Scientist
24 hours ago
Senior AI Data Scientist (6+ years)
Location: Mumbai
• Work Type: Onsite
• Team: Data & AI
• Reporting to: CTO
Company: Brandbuddiez
About the Role
Were looking for a hands-on Senior AI Data Scientist to lead endtoend development of ML/AI solutionsfrom problem framing and data strategy to model deployment and measurable business impact. Youll partner with engineering, product, and business stakeholders to ship reliable models in production and mentor a small team.
What Youll Do
- Own the full ML lifecycle for 12 highimpact problem areas (discovery delivery iteration).
- Translate ambiguous business problems into testable hypotheses and modelable objectives.
- Design, train, evaluate, and ship models (incl. LLM/RAG, forecasting, ranking, NLP/CV as needed).
- Build robust offline/online evaluation (A/B tests, counterfactuals, guardrails, humanintheloop).
- Productionize models with softwareengineering rigor (versioning, CI/CD, monitoring, observability).
- Drive data quality: schema design, feature stores, labeling strategy, data contracts, governance.
- Communicate clearly with execs and nontechnical partners; influence roadmaps with data.
Minimum Qualifications
- 6+ years in applied ML/Data Science with production impact (consumer or enterprise).
- Strong statistical foundations (causal inference, experimental design, uncertainty, metrics).
- Proficiency in Python and scientific stack (pandas, NumPy, scikitlearn); SQL fluency.
- Experience training and evaluating modern ML (treebased methods, GLMs, embeddings, deep learning).
- Experience deploying to production with at least one MLOps stack (e.g., MLflow, Kubeflow, SageMaker, Vertex, Databricks) and containerized services (Docker/Kubernetes).
- Track record of partnering with engineering and product; excellent written and verbal communication.
Preferred (Nice to Have)
- LLMs and Retrieval Augmented Generation (RAG): prompt/retrieval design, evaluation, safety/guardrails.
- Generative AI: finetuning, adapters/LoRA, instruction tuning, synthetic data, vector DBs (FAISS/PGV/Chroma).
- Recsys/ranking, timeseries forecasting, or causal ML for growth/marketing experimentation.
- Data engineering exposure (dbt, Spark, Kafka) and production telemetry/monitoring (Prometheus, Grafana, Evidently).
- Experience with privacy, responsible AI, and model risk management.
Core Tech & Tools (illustrative)
- Languages: Python, SQL.
- Libraries: scikitlearn, XGBoost/LightGBM, PyTorch/TensorFlow, Hugging Face, Ray.
- Data: Snowflake/BigQuery/Redshift, dbt, Airflow.
- MLOps: MLflow, Weights & Biases, Docker, Kubernetes, Feast/Featureform.
- Serving: FastAPI, gRPC, AWS/GCP/Azure serverless, vector DBs.
- Analytics/Experimentation: Amplitude/GA, Optimizely/Statsig, A/B testing frameworks.
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