Senior Data Scientist (6-12 Years)

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 (., propensity scoring, DiD) to measure true impact.

• Explain ability, Fairness & Compliance: Apply model cards, SHAP/LIME, bias

checks, PII handling, anonymization/pseudonymization, and align with

applicable data privacy regulations (., 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.

• Experience

  6 - 12 Years

• No. of Openings

  1

• Education

  Graduate

• Role

  Data Scientist

• Industry Type

  IT-Hardware & Networking / IT-Software / Software Services

• Gender

  [ Male / Female ]

• Job Country

  India

• Type of Job

  Full Time

• Work Location Type

  Work from Office