Senior AI Data Scientist

2 days ago

Borivli, Maharashtra, India Brandbuddiez Technologies Full time ₹ 12,00,000 - ₹ 36,00,000 per year

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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