Job Summary

Role: Senior Data Scientist

To lead advanced data science initiatives, develop predictive models, and drive data-driven decision-making by extracting meaningful insights from complex datasets, enabling business growth and innovation.

Key Responsibilities

Key Responsibilities

Forecasting & Modeling

- Design and deploy production-grade forecasting solutions using statistical models (ARIMA, ETS, BSTS) and ML approaches (XGBoost, LightGBM, neural networks)
- Engineer sophisticated features: lag features, rolling statistics, external signals, calendar effects, and domain-specific transformations
- Implement forecast reconciliation and hierarchical aggregation for complex business structures
- Establish rigorous evaluation frameworks: backtesting, time series cross-validation, accuracy metrics, prediction intervals, and drift monitoring

Software Engineering & Infrastructure

- Write production-grade Python and R code with modular architecture, comprehensive testing, error handling, and documentation
- Build and maintain sophisticated R Shiny applications with integrated JavaScript components
- Orchestrate ML pipelines using Kubeflow for automated training, validation, deployment, experiment tracking, and model versioning
- Manage infrastructure as code: Databricks workspaces, Azure resources, CI/CD pipelines (GitHub Actions, Azure DevOps), containerization, and secrets management

Analysis, Debugging & Monitoring

- Troubleshoot complex issues across the full stack: data pipeline failures, model degradation, API errors, and integration problems
- Implement continuous monitoring: automated data quality checks, feature drift detection, performance tracking, and alerting systems
- Conduct root cause analysis of forecast errors, identify data anomalies, validate business logic, and communicate findings clearly

Skill Requirements

Required Qualifications

Technical Foundation

- Education & Experience:



Master's or PhD with 3+ years delivering end-to-end data science solutions in production
- Programming: Strong Python, R and SQL proficiency
- Forecasting Expertise: Time series decomposition, seasonality, trend analysis, ensemble methods, probabilistic forecasting, hierarchical reconciliation
- Data Engineering: Databricks/Spark/PySpark, Delta Lake, ETL/ELT design, job orchestration, performance tuning
- KNIME: Building analytical workflows, data preprocessing, model pipelines, and system integration

End-to-End Capabilities

- MLOps: Kubeflow pipeline orchestration, experiment tracking, model registry, automated deployment
- Software Engineering: Git workflows, code reviews, testing frameworks (pytest, testthat), modular design, documentation
- Application Development: Build RESTful APIs and R Shiny applications from scratch; handle authentication, deployment, and optimization
- Cloud Infrastructure: Azure services (Databricks, Blob Storage, Data Factory, Key Vault, Functions), container orchestration, CI/CD

Essential Soft Skills

- Autonomy: Self-starter who can take vague requirements and independently drive projects from concept to production
- Problem-Solving: Systematic debugging across the full stack—from source data to infrastructure
- Business Acumen: Translate business needs into technical solutions; understand when "valuable enough" beats "perfect"
- Communication: Present technical work clearly to non-technical audiences; influence decisions with data; write comprehensive documentation
- Collaboration: Work effectively with cross-functional teams while maintaining end-to-end ownership

Must Have Skill

Azure Cloud Services,Databricks,Kubeflow,PySpark,Python,RESTful API

Good to Have Skill

Other Requirements

Preferred (Nice-to-have)

- SAP HANA/BW experience, advanced Kubeflow capabilities, Terraform, Kubernetes, PowerBI/Tableau integration, data governance frameworks, multilingual capability

📌 Senior Data Scientist (India)
🏢 HCLTech
📍 India