Senior Data Scientist

About TechRBM :

TechRBM is a fast-growing digital transformation partner to global enterprises, delivering solutions across AI, Cloud, Data Engineering, and Automation. We're scaling from 120+ to 300-500 professionals and are building a high-impact Data Science team to ship measurable business outcomes for clients in BFSI, Retail, Healthcare, Manufacturing, and High-Tech.

Role Overview :

We're hiring a Senior Data Scientist who can own end-to-end problem solving-from business discovery and hypothesis design to model deployment and post-production monitoring. You will partner with product, engineering, and client stakeholders to build production-grade ML/AI and GenAI solutions on AWS/Azure/GCP and mentor a small pod (2-5) of data scientists/ML engineers.

Key Responsibilities :

- Business & Problem Framing : Engage with client stakeholders to translate objectives into measurable DS/ML use cases, define success metrics (ROI, adoption, accuracy, latency), and create experiment plans.

- Data Strategy & Feature Engineering : Own data acquisition, quality checks, EDA, and feature pipelines across SQL/Spark/Databricks; collaborate with Data Engineering for robust ingestion and transformation (Airflow/dbt).

- Modeling : Build, tune, and compare models for supervised/unsupervised learning, time-series forecasting, NLP/CV, and GenAI (RAG, fine-tuning, prompt-engineering) using Python (pandas, NumPy, scikit-learn, XGBoost/LightGBM), PyTorch/TensorFlow, Hugging Face.

- MLOps & Deployment : Productionize via MLflow/DVC, model registry, CI/CD (GitHub/GitLab), containers (Docker/Kubernetes), and cloud ML platforms (SageMaker/Azure ML/Vertex AI). Expose services via FastAPI/Flask; implement monitoring for drift, data quality, and model performance.

- Experimentation & Causality : Design and analyze A/B tests, apply causal inference techniques (e.g., propensity scoring, DiD) to measure true impact.

- Explainability, Fairness & Compliance : Apply model cards, SHAP/LIME, bias checks, PII handling, anonymization/pseudonymization, and align with applicable data privacy regulations (e.g., GDPR/DPDP).

- Visualization & Storytelling : Build insights dashboards (Tableau/Power BI/Plotly) and communicate recommendations to senior business and technical stakeholders.

- Collaboration & Leadership : Mentor juniors, conduct code and research reviews, contribute to standards, and support solutioning during pre-sales/POCs.

Required Skills & Experience :

- Experience : 7-10 years overall, with 5+ years in applied ML/Data Science delivering models to production for enterprise clients.

- Programming & Data : Expert Python, advanced SQL, and hands-on with Spark/Databricks. Strong software practices (testing, typing, packaging).

- ML/AI Stack : scikit-learn, XGBoost/LightGBM; PyTorch or TensorFlow; NLP (spaCy, Transformers, embeddings), vector DBs (FAISS/Pinecone), LangChain/LlamaIndex for RAG.

- Cloud & MLOps : Real-world deployments on AWS/Azure/GCP using SageMaker/Azure ML/Vertex AI; MLflow, model registry, feature store, Docker/K8s, and CI/CD.

- Experimentation & Analytics : A/B testing, Bayesian/ frequentist methods, causal inference, statistical rigor.

- Visualization & Communication : Storytelling with data; Tableau/Power BI/Plotly, executive-ready presentations.

- Domain Exposure (nice-to-have) : BFSI risk/collections/CLV, retail demand/personalization, healthcare claims/clinical NLP, manufacturing quality/predictive maintenance.

- Bonus : Recommenders, time-series, graph ML, optimization (OR), reinforcement learning, geospatial analytics.

Education & Certifications :

- Bachelor's/Master's in Computer Science, Data Science, Statistics, Applied Math, or related field.

- Preferred certifications : AWS/Azure/GCP ML, Databricks, TensorFlow or PyTorch.