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.(ref : hirist.tech)